Taiwan

We address the challenging task of Localization via Embodied Dialog (LED). Given a dialog from two agents, an Observer navigating through an unknown environment and a Locator who is attempting to identify the Observer&#39;s location, the goal is to predict the Observer&#39;s final location in a map of the environment. We present the novel LED-Bert architecture and demonstrate an effective pre-training strategy from disembodied web data which allows our method to achieve state of the art performance on the task. Additionally during experimentation we find that using a graph-based map representation is more effective than the top-down 2D maps used in prior works.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.36/)

IJCNLP-AACL 2022

Transformer-based Localization from Embodied Dialog with Large-scale Pre-training

localization

navigation

multi-agent

embodied ai

dialog

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP).

A registration is required to access this event. Access to the event site will be granted to all registered attendees on **November 16th**. If you have not already done so, please register using the link below:
 
 https://conference.iis.sinica.edu.tw/servlet/Register?ConferenceID=411

Registration Is Required

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 

# 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). 

We address the challenging task of Localization via Embodied Dialog (LED). Given a dialog from two agents, an Observer navigating through an unknown environment and a Locator who is attempting to identify the Observer's location, the goal is to predict the Observer's final location in a map of the environment. We present the novel LED-Bert architecture and demonstrate an effective pre-training strategy from disembodied web data which allows our method to achieve state of the art performance on the task. Additionally during experimentation we find that using a graph-based map representation is more effective than the top-down 2D maps used in prior works.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.36/)

technical paper

BERT-like language models (LMs), when exposed to large unstructured datasets, are known to learn and sometimes even amplify the biases present in such data. These biases generally reflect social stereotypes with respect to gender, race, age, and others. In this paper, we analyze the variations in gender and racial biases in BERT, a large pre-trained LM, when exposed to different demographic groups. Specifically, we investigate the effect of fine-tuning BERT on text authored by historically disadvantaged demographic groups in comparison to that by advantaged groups. We show that simply by fine-tuning BERT-like LMs on text authored by certain demographic groups can result in the mitigation of social biases in these LMs against various target groups.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.38/)

Demographic-Aware Language Model Fine-tuning as a Bias Mitigation Technique

Conversational AI systems can engage in unsafe behaviour when handling users' medical queries that may have severe consequences and could even lead to deaths.
Systems therefore need to be capable of both recognising the seriousness of medical inputs and producing responses with appropriate levels of risk. 
We create a corpus of human written English language medical queries and the responses of different types of systems. We label these with both crowdsourced and expert annotations.
While individual crowdworkers may be unreliable at grading the seriousness of the prompts, their aggregated labels tend to agree with professional opinion to a greater extent on identifying the medical queries and recognising the risk types posed by the responses.Results of classification experiments suggest that, while these tasks can be automated, caution should be exercised, as errors can potentially be very serious.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.30/)

Risk-graded Safety for Handling Medical Queries in Conversational AI

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.44/)

Modeling Document-level Temporal Structures for Building Temporal Dependency Graph

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.23/)

Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics

To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i.e. storylines) to guide story generation. Such prior works mostly employ end-to-end neural generation models to predict event sequences for a story. However, such generation models struggle to guarantee the narrative coherence of separate events due to the hallucination problem, and additionally the generated event sequences are often hard to control due to the end-to-end nature of the models. To address these challenges, we propose NGEP, an novel event planning framework which generates an event sequence by performing inference on an automatically constructed event graph and enhances generalisation ability through a neural event advisor. We conduct a range of experiments on multiple criteria, and the results demonstrate that our graph-based neural framework outperforms the state-of-the-art (SOTA) event planning approaches, considering both the performance of event sequence generation and the effectiveness on the downstream task of story generation.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.24/)

NGEP: A Graph-based Event Planning Framework for Story Generation

Pre-trained language models such as KoBART often fail to generate perfect interrogative sentences when they are applied to Korean question generation. 
This is mainly due to the fact that the language models are trained with declarative sentences, but not with interrogative sentences. 
Therefore, this paper proposes a novel post-training of KoBART to enhance it for Korean question generation. 
The enhancement of KoBART is accomplished in three ways: 
(i) introduction of question infilling objective to KoBART to enforce it to focus more on the structure of interrogative sentences, 
(ii) augmentation of training data for question generation with another MRC data from AI-Hub to cope with the lack of training instances for post-training, 
(iii) introduction of Korean spacing objective to make KoBART understand the linguistic features of Korean. 
Since there is no standard data set for Korean question generation, this paper also proposes KorQuAD-QG, 
a new data set for this task, to verify the performance of the proposed post-training. 
Our code are publicly available at https://github.com/gminipark/post_training_qg.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.26/)

Post-Training with Interrogative Sentences for Enhancing BART-based Korean Question Generator

A bottleneck to developing Semantic Parsing (SP) models is the need for a large volume of human-labeled training data. Given the complexity and cost of human annotation for SP, labeled data is often scarce, particularly in multilingual settings. Large Language Models (LLMs) excel at SP given only a few examples, however LLMs are unsuitable for runtime systems which require low latency. In this work, we propose CLASP, a simple method to improve low-resource SP for moderate-sized models: we generate synthetic data from AlexaTM 20B to augment the training set for a model 40x smaller (500M parameters). We evaluate on two datasets in low-resource settings: English PIZZA, containing either 348 or 16 real examples, and mTOP cross-lingual zero-shot, where training data is available only in English, and the model must generalize to four new languages. On both datasets, we show significant improvements over strong baseline methods.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.56/)

CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.55/)

An Empirical Study of Pipeline vs. Joint approaches to Entity and Relation Extraction

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.14/)

An Effective Post-training Embedding Binarization Approach for Fast Online Top-K Passage Matching

Topic-sensitive query set expansion is an important area of research that aims to improve search results for information retrieval. It is particularly crucial for queries related to sensitive and emerging topics. In this work, we describe a method for query set expansion about emerging topics using vector space interpolation. We use a transformer model called OPTIMUS, which is suitable for vector space manipulation due to its variational autoencoder nature. One of our proposed methods -- Dirichlet interpolation shows promising results for query expansion. Our methods effectively generate new queries about the sensitive topic by incorporating set-level diversity, which is not captured by traditional sentence-level augmentation methods such as paraphrasing or back-translation.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.50/)

Transformer-based Localization from Embodied Dialog with Large-scale Pre-training

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Transformer-based Localization from Embodied Dialog with Large-scale Pre-training

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Next from IJCNLP-AACL 2022

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