We propose a method that ensembles $N$-best hypotheses to improve natural language generation. 
Previous studies have achieved notable improvements in generation quality by explicitly reranking $N$-best candidates. 
These studies assume that there exists a hypothesis of higher quality. 
We expand the assumption to be more practical as there exist \emph{partly} higher quality hypotheses in the $N$-best yet they may be imperfect as the entire sentences. 
By merging these high-quality fragments, we can obtain a higher-quality output than the single-best sentence. 
Specifically, we first obtain $N$-best hypotheses and conduct token-level quality estimation. 
We then apply tokens that should or should not be present in the final output as lexical constraints in decoding. 
Empirical experiments on paraphrase generation, summarisation, and constrained text generation confirm that our method outperforms the strong $N$-best reranking methods.

Self-Ensemble of N-best Generation Hypotheses by Lexically Constrained Decoding

We tackle the problem of monolingual phrase alignment conforming to syntactic structures. The existing method formalises the problem as unordered tree mapping; hence, the alignment quality is easily affected by syntactic ambiguities. We address this problem by expanding the method to align parse forests rather than 1-best trees, where syntactic structures and phrase alignment are simultaneously identified. The proposed method achieves efficient alignment by mapping forests on a packed structure. The experimental results indicated that our method improves the phrase alignment quality of the state-of-the-art method by aligning forests rather than 1-best trees.

Monolingual Phrase Alignment as Parse Forest Mapping

Monolingual word alignment is crucial to model semantic interactions between sentences.In particular, null alignment, a phenomenon in which words have no corresponding counterparts, is pervasive and critical in handling semantically divergent sentences. Identification of null alignment is useful on its own to reason about the semantic similarity of sentences by indicating there exists information inequality. To achieve unbalanced word alignment that values both alignment and null alignment, this study shows that the family of optimal transport (OT), i.e., balanced, partial, and unbalanced OT, are natural and powerful approaches even without tailor-made techniques.Our extensive experiments covering unsupervised and supervised settings indicate that our generic OT-based alignment methods are competitive against the state-of-the-arts specially designed for word alignment, remarkably on challenging datasets with high null alignment frequencies.

<iframe src="https://app.sli.do/event/kgQkh7cPzt9VEM52HG3MJu/embed/polls/e1df4293-a740-40ee-add4-dedc8e2ece16" width="300" height="400"></iframe>

Unbalanced Optimal Transport for Unbalanced Word Alignment

This study addresses the automatic generation of distractors for English fill-in-the-blank exercises in the entrance examinations for Japanese universities. While previous studies applied the same method to all questions, actual entrance examinations have multiple question types that reflect the purpose of the questions. Therefore, we define three types of questions (grammar, function word, and context) and propose a method to generate distractors according to the characteristics of each question type. Experimental results on 500 actual questions show the effectiveness of the proposed method for both automatic and manual evaluation.

Distractor Generation for Fill-in-the-Blank Exercises by Question Type

Controllable text simplification is a crucial assistive technique for language learning and teaching. 
One of the primary factors hindering its advancement is the lack of a corpus annotated with sentence difficulty levels based on language ability descriptions. 
To address this problem, we created the CEFR-based Sentence Profile (CEFR-SP) corpus, containing 17k English sentences annotated with the levels based on the Common European Framework of Reference for Languages assigned by English-education professionals. 
In addition, we propose a sentence-level assessment model to handle unbalanced level distribution because the most basic and highly proficient sentences are naturally scarce. 
In the experiments in this study, our method achieved a macro-F1 score of 84.5% in the level assessment, thus outperforming strong baselines employed in readability assessment.

CEFR-Based Sentence Difficulty Annotation and Assessment

Controllable text simplification assists language learners by automatically rewriting complex sentences into simpler forms of a target level. However, existing methods tend to perform conservative edits that keep complex words intact. To address this problem, we employ lexically constrained decoding to encourage rewriting. Specifically, the proposed method predicts edit operations conditioned to a target level and creates positive/negative constraints for words that should/should not appear in an output sentence. The experimental results confirm that our method significantly outperforms previous methods and demonstrates a new state-of-the-art performance.

Lexically Constrained Decoding with Edit Operation Prediction for Controllable Text Simplification

We propose a method for controlling the difficulty of a sentence based on deep reinforcement learning. Although existing models are trained based on the word-level difficulty, the sentence-level difficulty has not been taken into account in the loss function. Our proposed method generates sentences of appropriate difficulty for the target audience through reinforcement learning using a reward calculated based on the difference between the difficulty of the output sentence and the target difficulty. Experimental results of English text simplification show that the proposed method achieves a higher performance than existing approaches. Compared to previous studies, the proposed method can generate sentences whose grade-levels are closer to those of human references estimated using a fine-tuned pre-trained model.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.49/)

Controllable Text Simplification with Deep Reinforcement Learning

We propose a method to distill language-agnostic meaning embeddings from multilingual sentence encoders for unsupervised quality estimation of machine translation.
Our method facilitates that the meaning embeddings focus on semantics by adversarial training that attempts to eliminate language-specific information.
Experimental results on unsupervised quality estimation reveal that our method achieved higher correlations with human evaluations.

Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation

We create a large-scale dialogue corpus that provides pragmatic paraphrases to advance technology for understanding the underlying intentions of users. 
While neural conversation models acquire the ability to generate fluent responses through training on a dialogue corpus, previous corpora have mainly focused on the literal meanings of utterances. 
However, in reality, people do not always present their intentions directly.
For example, if a person said to the operator of a reservation service 'I don't have enough budget.', they, in fact, mean 'please find a cheaper option for me.'
Our corpus provides a total of 71,498 indirect--direct utterance pairs accompanied by a multi-turn dialogue history extracted from the MultiWoZ dataset. 
In addition, we propose three tasks to benchmark the ability of models to recognize and generate indirect and direct utterances. 
We also investigated the performance of state-of-the-art pre-trained models as baselines.

{DIRECT}: Direct and Indirect Responses in Conversational Text Corpus

We create a large-scale dialogue corpus that provides pragmatic paraphrases to advance technology for understanding the underlying intentions of users. 
While neural conversation models acquire the ability to generate fluent responses through training on a dialogue corpus, previous corpora have mainly focused on the literal meanings of utterances. 
However, in reality, people do not always present their intentions directly.
For example, if a person said to the operator of a reservation service "I don't have enough budget.", they, in fact, mean "please find a cheaper option for me."
Our corpus provides a total of 71,498 indirect--direct utterance pairs accompanied by a multi-turn dialogue history extracted from the MultiWoZ dataset. 
In addition, we propose three tasks to benchmark the ability of models to recognize and generate indirect and direct utterances. 
We also investigated the performance of state-of-the-art pre-trained models as baselines.

**Are you attending this poster session virtually?** 
In-person printed posters are available for in-person attendees only.
 
**Are you attending this poster session in person?** 
Hybrid posters are displayed in the East Foyer.

PS2 (In-person) Posters, Demo, Industry, Findings

poster

**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.

### [Program](https://sites.google.com/view/starsem2023/schedule)
To view Day 2 Live Session Recording click [**here**](https://underline.io/events/395/sessions?eventSessionId=15301&searchGroup=lecture) The 12th Joint Conference on Lexical and Computational Semantics (SEM 2023) is organized and sponsored by SIGLEX, the Special Interest Group of the ACL. SEM brings together researchers interested in the semantics of natural languages and its computational modeling. The conference embraces data-driven, neural, and probabilistic approaches, as well as symbolic approaches and everything in between; practical applications as well as theoretical contributions are welcome. The long-term goal of SEM is to provide a stable forum for the growing number of NLP researchers working on all aspects of semantics of (many and diverse!) natural languages. For more details visit our workshop website: https://sites.google.com/view/starsem2023
#### [Visit GatherTown](https://app.gather.town/app/5OscLswEEuYf2Psb/ACL2023)

W2: The 12th Joint Conference on Lexical and Computational Semantics (*SEM)_Day 1

workshop paper

### 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.

Poster Session 7

Virtual Poster Session 1

**Topics:** Multilinguality 
NLP Applications 
Natural Language Generation 
Question Answering 
Resources and Evaluation 
Semantics: Lexical, Sentence level, Textual Inference and Other areas 
Speech, Vision, Robotics, Multimodal Grounding 
Summarization 
Theme Track 
Unsupervised and Weakly-Supervised Methods in NLP

Poster Session 14

## 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.

**Chair:** Sanja Štajner **Organizers:** Sanja Štajner, Horacio Saggion, Wei Xu, Marcos Zampieri, Matthew Shardlow, Daniel Ferrés, Kai North and Kim Cheng Sheang 
**Invited speakers**: [Matt Huenerfauth](https://underline.io/speakers/154911-matt-huenerfauth) and [Sowmya Vajjala](https://underline.io/speakers/212907-sowmya-vajjala) Please visit our [**website**](https://www.taln.upf.edu/pages/tsar2022-ws/) for more information and detailed schedule.


**RocketChat channel for this workshop:** https://emnlp2022.rocket.chat/channel/workshop-23-tsar
 
Sponsored by: 
[![](https://assets.underline.io/uploads/markdown_image/1/image/8938dc12f9b413dddfcc39a44d0b59af.png)](https://www.frontiersin.org/)

W23 (TSAR) Workshop on Text Simplification, Accessibility, and Readability

**Session Chair:**
 Preslav Nakov

Session 3D: Question Answering and Generation

technical paper

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

IJCNLP-AACL 2022

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). 

This is an in-person poster sessions. If you are a virtual attendee you can browse the posters below and ask questions in the QA box next to the poster **Chair:** Hyun-Je Song

PS2 - Machine Translation and translation aids

[![](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.



[![](https://s3.amazonaws.com/pf-upload-01/u-59356/0/2021-09-18/me23j72/button_workshop-website.png)](https://sites.google.com/view/3rdnlp4convai/) 

W3: 3rd Workshop on NLP for Conversational AI (NLP4ConvAI)

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

<iframe style="width:700px;height:400px" src="https://online.fliphtml5.com/ebtyf/ceby/" seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe>

Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Yuki Arase

10

8

SHORT BIO

Presentations

Self-Ensemble of N-best Generation Hypotheses by Lexically Constrained Decoding

Monolingual Phrase Alignment as Parse Forest Mapping

Unbalanced Optimal Transport for Unbalanced Word Alignment

Distractor Generation for Fill-in-the-Blank Exercises by Question Type

CEFR-Based Sentence Difficulty Annotation and Assessment

Lexically Constrained Decoding with Edit Operation Prediction for Controllable Text Simplification

Controllable Text Simplification with Deep Reinforcement Learning

Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation

{DIRECT}: Direct and Indirect Responses in Conversational Text Corpus

{DIRECT}: Direct and Indirect Responses in Conversational Text Corpus

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