Recent research shows that more data and larger models can provide more accurate solutions to natural language problems that require reasoning. However, models can easily fail to provide solutions in unobserved levels of compositional complexity because they might not obtain the level of abstraction needed for generalizability. To alleviate this issue, we propose to train the models with neuro-symbolic techniques that can exploit the logical rules of reasoning as constraints and provide additional supervision sources to the model. Training models to adhere to the regulations of reasoning pushes them to make more effective abstractions needed for generalizability and transfer learning. We focus on a challenging problem of spatial reasoning over text, and our results on multiple benchmarks confirm our hypothesis of effective domain transfer based on neuro-symbolic training. We utilize our symbolic training approach on multiple commonly used language models.

Tuning Language Models with Spatial Logic for Complex Reasoning

This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming(ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions' prior probability, confidence(uncertainty), and the models' expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets.

Consistent Joint Decision-Making with Heterogeneous Learning Models

This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming(ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions' prior probability, confidence~(uncertainty), and the models' expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets.

The existing work on vision and language navigation mainly relies on navigation-related losses to establish the connection between vision and language modalities, neglecting aspects of helping the navigation agent build a deep understanding of the visual environment.
In our work, we provide indirect supervision to the navigation agent through a hint generator that provides detailed visual descriptions.
The hint generator assists the navigation agent in developing a global understanding of the visual environment. It directs the agent's attention toward related navigation details, including the relevant sub-instruction, potential challenges in recognition and ambiguities in grounding, and the targeted viewpoint description. 
To train the hint generator, we construct a synthetic dataset based on landmarks in the instructions and visible and distinctive objects in the visual environment.
We evaluate our method on the R2R and R4R datasets and achieve state-of-the-art on several metrics. 
The experimental results demonstrate that generating hints not only enhances the navigation performance but also helps improve the agent's interpretability.

NavHint: Vision and Language Navigation Agent with a Hint Generator

Compositional generalization, the ability of intelligent models to extrapolate understanding of components to novel compositions, is a fundamental yet challenging facet in AI research, especially within multimodal environments. In this work, we address this challenge by exploiting the syntactic structure of language to boost compositional generalization. This paper elevates the importance of syntactic grounding, particularly through attention-masking techniques derived from text input parsing. We introduce and evaluate the merits of using syntactic information in the multimodal grounding problem. Our results on grounded compositional generalization underscore the positive impact of dependency parsing across diverse tasks when utilized with Weight Sharing across the Transformer encoder. The results push the state-of-the-art in multimodal grounding and parameter-efficient modeling and provide insights for future research.

Syntax-Guided Transformers: Elevating Compositional Generalization and Grounding in Multimodal Environments

In this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help to reason over the states of involved entities in a procedural text. We consider a deep semantic parser~(TRIPS) and semantic role labeling as two sources of semantic parsing knowledge. 
First, we propose PROPOLIS, a symbolic parsing-based procedural reasoning framework.
Second, we integrate semantic parsing information into state-of-the-art neural models to conduct procedural reasoning.
Our experiments indicate that explicitly incorporating such semantic knowledge improves procedural understanding. This paper presents new metrics for evaluating procedural reasoning tasks that clarify the challenges and identify differences among neural, symbolic, and integrated models.

The Role of Semantic Parsing in Understanding Procedural Text

Language understanding is essential for the navigation agent to follow instructions. We observe two kinds of issues in the instructions that can make the navigation task challenging: 1. The mentioned landmarks are not recognizable by the navigation agent due to the different vision abilities of the instructor and the modeled agent. 2. The mentioned landmarks are applicable to multiple targets, thus not distinctive for selecting the target among the candidate viewpoints.To deal with these issues, we design a translator module for the navigation agent to convert the original instructions into easy-to-follow sub-instruction representations at each step. The translator needs to focus on the recognizable and distinctive landmarks based on the agent's visual abilities and the observed visual environment.To achieve this goal, we create a new synthetic sub-instruction dataset and design specific tasks to train the translator and the navigation agent.We evaluate our approach on Room2Room~(R2R), Room4room~(R4R), and Room2Room Last (R2R-Last) datasets and achieve state-of-the-art results on multiple benchmarks.

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VLN-Trans: Translator for the Vision and Language Navigation Agent

In this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help reasoning over the states of involved entities in a procedural text. We consider a deep semantic parser~(TRIPS) and semantic role labeling as two sources of semantic parsing knowledge. 
First, we propose PROPOLIS, a symbolic parsing-based procedural reasoning framework.
Second, we integrate semantic parsing information into state-of-the-art neural models to conduct procedural reasoning.
Our experiments indicate that explicitly incorporating such semantic knowledge improves procedural understanding. This paper presents new metrics for evaluating procedural reasoning tasks that clarify the challenges and identify differences among neural, symbolic, and integrated models.

Recent research has shown that integrating domain knowledge in deep learning architectures is effective -- it helps reduce the amount of required data, supports reasoning over complex tasks, thus improving the quality of the models' output, and improves the interpretability of models. However, the research community is missing a convened benchmark for systematically evaluating knowledge integration methods.
In this work, we create a collection of benchmarks that includes ten tasks in the domains of natural language processing and computer vision. In all cases, we model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
We report the results of these models using a new set of extended evaluation criteria in addition to the task performances for a more in-depth analysis. This effort provides a framework for a more comprehensive and systematic comparison of constraint integration techniques and for identifying the related research challenges. It will facilitate further research for alleviating some problems of state-of-the-art neural models.

GLUECons: A Generic Benchmark for Learning Under Constraints

Recent research shows synthetic data as a source of supervision helps pretrained language models (PLM) transfer learning to new target tasks/domains. However, this idea is less explored for spatial language. We provide two new data resources on multiple spatial language processing tasks. The first dataset is synthesized for transfer learning on spatial question answering (SQA) and spatial role labeling (SpRL). Compared to previous SQA datasets, we include a larger variety of spatial relation types and spatial expressions. Our data generation process is easily extendable with new spatial expression lexicons. The second one is a real-world SQA dataset with human-generated questions built on an existing corpus with SPRL annotations. This dataset can be used to evaluate spatial language processing models in realistic situations. We show pretraining with automatically generated data significantly improves the SOTA results on several SQA and SPRL benchmarks, particularly when the training data in the target domain is small.

Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning

This work investigates the challenge of learning and reasoning for Commonsense Question Answering given an external source of knowledge in the form of a knowledge graph (KG). We propose a novel graph neural network architecture, called Dynamic Relevance Graph Network (DRGN). DRGN operates on a given KG subgraph based on the question and answers entities and uses the relevance scores between the nodes to establish new edges dynamically for learning node representations in the graph network. This explicit usage of relevance as graph edges has the following advantages, a) the model can exploit the existing relationships, re-scale the node weights, and influence the way the neighborhood nodes' representations are aggregated in the KG subgraph, b) It potentially recovers the missing edges in KG that are needed for reasoning. Moreover, as a byproduct, our model improves handling the negative questions due to considering the relevance between the question node and the graph entities. Our proposed approach shows competitive performance on two QA benchmarks, CommonsenseQA and OpenbookQA, compared to the state-of-the-art published results.

Dynamic Relevance Graph Network for Knowledge-Aware Question Answering

We mainly designs a transformer-based navigation agent with modular design of orientation and vision reasoning. Also, we design specific pre-training tasks to distil more explicit spatial and visual knowledge, which is better utilized in the corresponding modules in our navigation agent.

LOViS: Learning Orientation and Visual Signals for Vision and Language Navigation

In this paper, we investigate the problem of vision and language navigation. To solve this problem, grounding the landmarks and spatial relations in the textual instructions into visual modality is important. We propose a neural agent named Explicit Object Relation Alignment Agent (EXOR), to explicitly align the spatial information in both instruction and the visual environment, including landmarks and spatial relationships between the agent and landmarks. Empirically, our proposed method surpasses the baseline by a large margin on the R2R dataset. We provide a comprehensive analysis to show our model’s spatial reasoning ability and explainability.

 Explicit Object Relation Alignment for Vision and Language Navigation

We study the challenge of learning causal reasoning over procedural text to answer "What if..." questions when external commonsense knowledge is required. We propose a novel multi-hop graph reasoning model to 1) efficiently extract a commonsense subgraph with the most relevant information from a large knowledge graph; 2) predict the causal answer by reasoning over the representations obtained from the commonsense subgraph and the contextual interactions between the questions and context. We evaluate our model on WIQA benchmark and achieve state-of-the-art performance compared to the recent models.

Relevant CommonSense Subgraphs for "What if..." Procedural Reasoning

We deal with the navigation problem where the agent follows natural language instructions while observing the environment.
Focusing on language understanding, we show the importance of spatial semantics in grounding navigation instructions into visual perceptions. 
We propose a neural agent that uses the elements of spatial configurations and investigate their influence on the navigation agent's reasoning ability. Moreover, we model the sequential execution order and align visual objects with spatial
configurations in the instruction. Our neural agent improves strong baselines on the seen environments and shows competitive performance on the unseen environments. Additionally, the experimental results demonstrate that explicit modeling of spatial semantic elements in the instructions can improve the grounding and spatial reasoning of the model.

Towards Navigation by Reasoning over Spatial Configurations

Tracking entities throughout a procedure described in a text is challenging due to the dynamic nature of the world described in the process. Firstly, we propose to formulate this task as a question answering problem. This enables us to use pre-trained transformer-based language models on other QA benchmarks by adapting those to the procedural text understanding. Secondly, since the transformer-based language models cannot encode the flow of events by themselves, we propose a Time-Stamped Language Model(TSLM) to encode event information in LMs architecture by introducing the timestamp encoding. Our model evaluated on the Propara dataset shows improvements on the published state-of-the-art results with a 3.1\% increase in F1 score. Moreover, our model yields better results on the location prediction task on the NPN-Cooking dataset. This result indicates that our approach is effective for procedural text understanding in general.

Time-Stamped Language Model: Teaching Language Models to Understand The Flow of Events

*Coffee Breaks will be provided between 10:30 and 11:00 and 15:30 and 16:00.* To view the workshop program in detail, click [here](https://splu-robonlp-2024.github.io/#schedule)

W27: SpLU-RoboNLP: Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics (SpLU-RoboNLP)

workshop paper

### Welcome!
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

You are required to register for this event. **Please register [here](https://2024.aclweb.org/registration). **

If you have already registered, please check your inbox for an email from Underline granting you access to ACL 2024 content.

Please register!

ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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[**Teleport to Gather**](https://app.gather.town/app/XDzSQW1b0mpTTAYr/EACL2024?spawnToken=os0ysYmnRMmrw1j9DuqC) 
Human languages are inherently ambiguous and understanding language input is subject to interpretation and complex contextual dependencies. Nevertheless, the main body of research in NLP is still based
on the assumption that ambiguities and other types of underspecification can and have to be re-solved.
This workshop will provide a platform for research that embraces variability in human language and aims to represent and evaluate the uncertainty that arises from it, and from modeling tools themselves. UncertaiNLP brings together researchers embracing sources of uncertainty from human language and NLP tools; harnessing them for improved NLP. [**Read more**](https://uncertainlp.github.io/)
 [**Invited Speakers**](https://uncertainlp.github.io/index) **Program Overview** 
09:00 - 09:10 Opening Remarks 
09:10 - 09:55 Keynote Talk - Chrysoula Zerva 
09:55 - 10:15 Presentation Session 1 
10:15 - 10:30 Poster Spotlights 
10:30 - 11:00 Coffee Break 
11:00 - 12:15 In-Person Poster Session 
12:15 - 13:15 Lunch Break 
13:15 - 14:00 Keynote Talk - Clara Meister 
14:00 - 15:30 Presentation Session 2 
15:30 - 16:00 Coffee Break 
16:00 - 16:30 Presentation Session 3 
16:30 - 17:15 Keynote Talk 
17:15 - 17:30 Closing Remarks [**Detailed Program**](https://uncertainlp.github.io/program)

W16: UncertaiNLPFirst Workshop on Uncertainty-Aware NLP (UncertaiNLP) 

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
#### [Conference Handbook](https://drive.google.com/file/d/13Wn38q0yev6U3RTgnf4Tn5T9GuAuZ-xW/view?usp=sharing) *downloadable*

To access the EACL 2024 event page you need to register [**here**](https://acl.swoogo.com/EACL2024). 

EACL 2024

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

In-Person Poster and Demo Session D

poster

Virtual Poster Session A

**Workshop Organizers:** Dieuwke Hupkes, Verna Dankers, Koustuv Sinha, Amirhossein Kazemnejad, Christos Christodoulopoulos, Ryan Cotterell and Elia Bruni
 *Coffee Break 10:30 am* 
*Lunch Break 12:30 pm* 
*Coffee Break 3:30 pm* [**Workshop Website**](https://genbench.org/workshop/) The ability to generalise is one of the primary desiderata of natural language processing (NLP). Yet, how "good generalisation" should be defined and evaluated is not well understood, nor are there common standards to evaluate generalisation. As a consequence, newly proposed models are usually not systematically tested for their ability to generalise. GenBench's mission is to make state-of-the-art generalisation testing the new status-quo in NLP. As a first step, we present a generalisation taxonomy, describing the underlying building blocks of generalisation in NLP. We use the taxonomy to do an elaborate review of over 400 generalisation papers, and we make recommendations for promising areas for the future. 
[**Keynote 1, by Anna Rogers**](https://genbench.org/workshop_programme/#0915-1000-am--keynote-1-by-anna-rogers) [**Keynote 2, by Adina Williams**](https://genbench.org/workshop_programme/#1115-1200-pm--keynote-2-by-adina-williams) [**Keynote 3, by Tatsunori Hashimoto**](https://genbench.org/workshop_programme/#1400-1445-pm--keynote-3-by-tatsunori-hashimoto) 
[**Schedule**](https://genbench.org/workshop_programme/)

 [**Go to Gather**](https://app.gather.town/app/ROH1myZvp9ba7a3C/EMNLP2023?spawnToken=_5ieSSlxSvuKGu7FtBcT)

W04: GenBench: The first workshop on generalisation (benchmarking) in NLP

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

With recent scaling of large pre-trained Transformer language models (LLMs), the scope of feasible NLP tasks has broadened. Significant recent work has focused on tasks that require some kind of natural language reasoning. A trajectory in question answering has led us from extraction-oriented datasets like SQuAD to “multi-hop” reasoning datasets like HotpotQA and StrategyQA. Although LLMs have shown remarkable performance on most NLP tasks, it is often unclear why their answers follow from what they know. To address this gap, a new class of explanation techniques has emerged which play an integral part in structuring the reasoning necessary to solve these datasets. For example, the chain-of-thought paradigm leverages explanations as vehicles for LLMs to mimic human reasoning processes. Entailment trees offer a way to ground multi-step reasoning in a collection of verifiable steps. Frameworks like SayCan bridge high-level planning in language and with low-level action trajectories. As a result, we see a confluence of methods blending explainable machine learning/NLP, classical AI (especially theorem proving), and cognitive science (how do humans structure explanations?). This workshop aims to bring together a diverse set of perspectives from these different traditions and attempt to establish common ground for how these various kinds of explanation structures can tackle a broad class of reasoning problems in natural language and beyond. For more details visit our workshop website: https://nl-reasoning-workshop.github.io/
#### [Visit GatherTown](https://app.gather.town/app/5OscLswEEuYf2Psb/ACL2023)

W8: The 1st Workshop on Natural Language Reasoning and Structured Explanations

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Language Grounding to Vision, Robotics, and Beyond 1

technical paper

Please feel free to view the Findings papers at your convenience. You are welcome to leave your comment or a question in the Chat box.

Findings

findings / work in progress

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

EACL 2023

Posters & Demos IV

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

**Topics:** Theme Track 
Natural Language Generation 
Question Answering 
Resources and Evaluation

Virtual Poster Session 16

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/9eec7d4a287ee18c278b08229290aa83.png)](https://drive.google.com/file/d/1OlPv6QBeo62VVTughj2jkiLeyHd1WnUt/view)
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/a3db7a768409f05192210d98601edb25.png)](https://emnlp2022.rocket.chat/)

To access this site you need to register. Please register [here](https://2022.emnlp.org/registration/).

Register here

EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Click on the Live Session Recording to view the session

VOS38 - Inference and Question Answering

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

To gain access to this event page you are required to register. Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

VOS7 - Multimodal and Grounded Language Acquisition, HRI

Posters: Language Grounding, Speech and Multimodality

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

Posters: Question Answering

SpLU-RoboNLP: Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

Registration is now open

IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

**Session Chair: ** Marek Rei

13B-Oral: Question Answering

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

**Whova App** 
Stay in touch with your fellow conference attendees via the [Whova App](https://whova.com/portal/webapp/nacon_202106/)

**Conference Structure**
https://2021.naacl.org/blog/conference-structure/

**Walkthrough video of how to NAACL 2021** 

Please take a moment to view this video explaining how to navigate the platform, attend sessions network with other attendees. 


<figure class="video_container">
 <iframe src="https://screencast-o-matic.com/watch/crhwbGVh3vx?v=6&ff=1&title=0&controls=1" width=640 height=350 frameborder="0" allowfullscreen="true"> </iframe>
</figure>

Parisa Kordjamshidi

17

22

SHORT BIO

Presentations

Tuning Language Models with Spatial Logic for Complex Reasoning

Consistent Joint Decision-Making with Heterogeneous Learning Models

Consistent Joint Decision-Making with Heterogeneous Learning Models

NavHint: Vision and Language Navigation Agent with a Hint Generator

Syntax-Guided Transformers: Elevating Compositional Generalization and Grounding in Multimodal Environments

The Role of Semantic Parsing in Understanding Procedural Text

VLN-Trans: Translator for the Vision and Language Navigation Agent

The Role of Semantic Parsing in Understanding Procedural Text

GLUECons: A Generic Benchmark for Learning Under Constraints

Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning

Dynamic Relevance Graph Network for Knowledge-Aware Question Answering

LOViS: Learning Orientation and Visual Signals for Vision and Language Navigation

Explicit Object Relation Alignment for Vision and Language Navigation

Relevant CommonSense Subgraphs for "What if..." Procedural Reasoning

Towards Navigation by Reasoning over Spatial Configurations

Time-Stamped Language Model: Teaching Language Models to Understand The Flow of Events

Stay up to date with the latest Underline news!

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