Computing the semantic similarity between two texts is crucial in various NLP tasks. For more than a decade, a framework, known as Semantic Textual Similarity (STS) has been used to test computational models of semantic similarity (Agirre et al., 2012). The STS evaluation framework assumes that a model that performs well for the general STS task should also perform well for specific application-oriented tasks. However, does this assumption indeed hold? This study empirically demonstrates that the answer is not always positive. We found a considerable gap between model performance in STS and each specific task. We identified three factors that contributed to the gap, namely, (i) sentence length distribution, (ii) vocabulary coverage, and (iii) granularity of gold-standard similarity scores. We believe that these findings will be considered in future research on semantic similarity.

Chat Translation Error Detection for Assisting Cross-lingual Communications

In this paper, we describe the development of a communication support system that detects erroneous translations to facilitate cross-lingual communications due to the limitations of current machine chat translation methods.
We trained an error detector as the baseline of the system and constructed a new Japanese–English bilingual chat corpus, BPersona-chat, which comprises multi-turn colloquial chats augmented with crowdsourced quality ratings.
The error detector can serve as an encouraging foundation for more advanced erroneous translation detection systems.

Humans use different wordings depending on the context to facilitate efficient communication.
For example, instead of completely new information, information related to the preceding context is typically placed at the sentence-initial position.
In this study, we analyze whether neural language models (LMs) can capture such discourse-level preferences in text generation.
Specifically, we focus on a particular aspect of discourse, namely the topic-comment structure.
To analyze the linguistic knowledge of LMs separately, we chose the Japanese language, a topic-prominent language, for designing probing tasks, and we created human topicalization judgment data by crowdsourcing.
Our experimental results suggest that LMs have different generalizations from humans; LMs exhibited less context-dependent behaviors toward topicalization judgment.
These results highlight the need for the additional inductive biases to guide LMs to achieve successful discourse-level generalization.

Topicalization in Language Models: A Case Study on Japanese

Human-like syntactic generalization abilities of neural language models (LMs) have received wide attention, but discourse-level knowledge in these models has been less explored. In this study, we analyze the LMs with respect to a particular aspect of discourse, topic-comment structure, a crucial part of text production. To separately analyze the knowledge of LMs on topic-comment structure, we chose the Japanese language, a topic-prominent language, for designing probing tasks. Experimental results suggest that LMs have different generalizations from humans in terms of the topic-comment structure. These results imply that LMs do not perform human-like discourse processing in text generation.

Neural Machine Translation (NMT) has shown drastic improvement in its quality when translating clean input, such as text from the news domain. However, existing studies suggest that NMT still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet. To make better use of NMT for cross-cultural communication, one of the most promising directions is to develop a model that correctly handles these expressions. Though its importance has been recognized, it is still not clear as to what creates the great gap in performance between the translation of clean input and that of UGC. To answer the question, we present a new dataset, PheMT, for evaluating the robustness of MT systems against specific linguistic phenomena in Japanese-English translation. Our experiments with the created dataset revealed that not only our in-house models but even widely used off-the-shelf systems are greatly disturbed by the presence of certain phenomena.

PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents

[![](https://assets.underline.io/uploads/markdown_image/1/image/d3ad15ab8ad93db2e42d31f48e79049f.png)](https://us06web.zoom.us/rec/play/QR1bjSINsXtoCwhY664nr5uaTfXm14Onn-Sga7UXDiB7Xfk_nIRJxAg2rtpbocm42p7a1evwF-L7lZ_I.LMv_oBcNWWJGQ2Ob?autoplay=true&startTime=1668939300000) 
[![](https://assets.underline.io/uploads/markdown_image/1/image/4fb65ba99e2b22744cbf88050198d474.png)](https://us06web.zoom.us/rec/play/H5ktPEVrL38GW8kc0uTOAcAA1NsSzLYfOiUleYV2t2PAfooBgSf_PJWFmeZo4eocHAWFOifUjAdmCoqM.Lf8MG-Crb7SVndZ_?autoplay=true&startTime=1668966531000)
 
 **Workshop Organizers:**
 Daniel Deutsch, Can Udomcharoenchaikit, Alex Gaskell, Marina Fomicheva, Steffen Eger, and Yang Gao

Third Workshop on Evaluation and Comparison of NLP Systems

workshop 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:** Jeska Buhmann

PS1 - Discourse and Pragmatics

poster

[![](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.

Student Research Workshop 1

**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).

POSTER22: Translation, Crosslingual and Multilingual Systems

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

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 and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Kaori Abe

5

25

SHORT BIO

Presentations

Chat Translation Error Detection for Assisting Cross-lingual Communications

Chat Translation Error Detection for Assisting Cross-lingual Communications

Topicalization in Language Models: A Case Study on Japanese

Topicalization in Language Models: A Case Study on Japanese

PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES