Taiwan

IJCNLP-AACL 2022

VISCOSE - a Kanji Dictionary Enriched with VISual, COmpositional, and SEmantic Information.

natural language annotation

language resources

lexical semantics

**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

ACL-IJCNLP 2021

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

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). 
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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:
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<br>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). 

We present three large-scale experiments on binary text matching classification task both in Chinese and English to evaluate the effectiveness and generalizability of random text perturbations as a data augmentation approach for NLP. It is found that the augmentation can bring both negative and positive effects to the test set performance of three neural classification models, depending on whether the models train on enough original training examples. This remains true no matter whether five random text editing operations, used to augment text, are applied together or separately. Our study demonstrates with strong implication that the effectiveness of random text perturbations is task specific and not generally positive.

Addresing Reproducibility in Natural Language Processing

Various approaches have been proposed for automated stance detection, including those that use machine and deep learning models and natural language processing techniques. However, their cross-dataset performance, the impact of sample size on performance, and experimental aspects such as runtime have yet to be compared, limiting what is known about the generalizability of prominent approaches. This paper presents a replication study of stance detection approaches on current benchmark datasets. Specifically, we compare six existing machine and deep learning stance detection models on three publicly available datasets. We investigate performance as a function of the number of samples, length of samples (word count), representation across targets, type of text data, and the stance detection models themselves. We identify the current limitations of these approaches and categorize their utility for stance detection under varying circumstances (e.g., size of text samples), which provides valuable insight for future research in stance detection.

Why sentence similarity benchmark is not predictive of application-oriented task performance?

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.

SMART: Sentences as Basic Units for Text Evaluation

Questioning Implicit Assumptions in our Evaluation Methodologies

GLARE: Generative Left-to-right AdversaRial Examples

 MasakhaNER: Named Entity Recognition Dataset for 20 African languages.

SUMEval shared task overview

NTREX-128 -- News Test References for MT Evaluation of 128 Languages

Significant progress has been made on Indonesian NLP. Nevertheless, exploration of the codemixing phenomenon in Indonesian is limited, despite many languages being frequently mixed with Indonesian. In this work, we explore codemixing in Indonesian with four embedded languages, i.e., English, Sundanese, Javanese, and Malay; and introduce IndoRobusta, a framework to evaluate and improve the code-mixing robustness. Our analysis shows that the pretraining corpus bias affects the model’s ability to better handle Indonesian-English codemixing rather than other local languages, despite having higher language diversity.

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