Taiwan

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IJCNLP-AACL 2022

When Cantonese NLP Meets Pre-training: Progress and Challenges

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.

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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.

tutorial

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:
<br>
<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). 

Multiplex Networks and AI Unveil the Influence of the Mental Lexicon on Picture Naming and its Failures by People Struck with Aphasia

Object Naming is an important task within the field of Language and Vision that consists of generating a correct and appropriate name for an object given an image. The ManyNames dataset uses real-world human annotated images with multiple labels, instead of just one. In this work, we describe the adaptation of this dataset (originally in English) to Catalan, by (i) machine-translating the English labels and
(ii) collecting human annotations for a subset of the original corpus and comparing both resources. Analyses reveal divergences in the lexical variation of the two sets showing potential problems of directly translated resources, particularly when there is no resource to a proper context, which in this case is conveyed by the image. The analysis also points to the impact of cultural factors in the naming task, which
should be accounted for in future cross-lingual naming tasks.

CAT ManyNames: A New Dataset for Object Naming in Catalan.

The Thesaurus Linguae Latinae (TLL) is a comprehensive monolingual dictionary that records contextualized meanings and usages of Latin words in antique sources at an unprecedented scale. We created a new dataset based on a subset of sense representations in the TLL, with which we finetuned the Latin BERT neural language model (Bamman and Burns, 2020) on a supervised Word Sense Disambiguation task. We observe that the contextualized BERT representations finetuned on TLL data score better than static embeddings used in a bidirectional LSTM classifier on the same dataset, and that our per-headword BERT models achieve higher and more robust performance than reported by Bamman and Burns (2020) based on data from a bilingual Latin dictionary. We discuss the differences in sense organizational principles between these two lexical resources, and report about our dataset construction and improved evaluation methodology.

Finetuning Latin BERT for Word Sense Disambiguation on the Thesaurus Linguae Latinae.

Definition modeling is the task to generate a valid definition for a given input term. This relatively novel task has been approached either with no context (i.e., given a word embedding alone) and, more recently, as word-in-context modeling. Despite their success, most works make little to no distinction between resources and their specific features (e.g., type and style of definitions, or quality of examples) when used for training. Given the high diversity lexicographic resources exhibit in terms of topic coverage, style and formal structure, it is desirable for downstream definition modeling to better understand which of them are better suited for the task. In this paper, we propose an empirical evaluation of the well-known lexical database WordNet, and specifically, its dictionary examples. We evaluate them both directly, by matching them against criteria for good dictionary writing, and indirectly, in the task of definition modeling. Our results suggest that WordNet’s dictionary examples could be improved by extending them in length, and incorporating prototypicality.

Putting WordNet’s Dictionary Examples in the Context of Definition Modelling: An Empirical Analysis.

A Frame-Based Model of Inherent Polysemy, Copredication and Argument Coercion.

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

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.

When Cantonese NLP Meets Pre-training: Progress and Challenges

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Multiplex Networks and AI Unveil the Influence of the Mental Lexicon on Picture Naming and its Failures by People Struck with Aphasia

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When Cantonese NLP Meets Pre-training: Progress and Challenges

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Next from IJCNLP-AACL 2022

Multiplex Networks and AI Unveil the Influence of the Mental Lexicon on Picture Naming and its Failures by People Struck with Aphasia

Similar lecture

Scaffolded input promotes atomic organization in the recurrent neural network language model

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads