Taiwan

We present a Japanese morphological corpus of sentences from 27 specialized domains for the two tasks of word segmentation and partof-speech tagging. Experiments on the corpus demonstrated that recent neural models with domain adaptation techniques and pretrained language models achieved accurate performance for the two tasks for many specialized domains

IJCNLP-AACL 2022

A Japanese Corpus of Many Specialized Domains for Word Segmentation and Part-of-Speech Tagging

word segmentation

part-of-speech tagging

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

BRACIS 2020

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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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). 

The distinction between mass nouns and count nouns has a long history in formal semantics, and linguists have been trying to identify the semantic properties defining the two classes. However, they also recognized that both can undergo meaning shifts and be used in contexts of a different type, via nominal coercion.
In this study, we present an approach to measure the meaning shift in count-mass coercion in English that makes use of static and contextualized word embedding distance. Our results show that the coercion shifts are detected only by a small subset of the traditional word embedding models and that the shifts detected by the contextualized embedding of BERT are more pronounced for mass nouns.

Exploring Nominal Coercion in Semantic Spaces with Static and Contextualized Word Embeddings.

In this presentation, we introduce a new approach that simulates the construction of phrasal meaning as an analogical process implemented with deep learning techniques. We propose a distributional representation of constructional phrases (namely Relational Embeddings) and a neural network model for generating new embeddings analogically rather than applying traditional compositional operations, which we call ANNE (Analogical Neural Network for Embeddings). 
We experimented with our analogical neural network to understand how it can generalize and be extendable to different scenarios. We argued that the proposed methodology could open the doors to new analyses in distributional semantics as well as in computational models of language processing.

Compositionality as an Analogical Process: Introducing ANNE.


Transformer-based language models are often trained on structured text where non-lexical markers of sentence and discourse structure (e.g., punctuation and casing) are present and used consistently. Transformers encode these markers and arguably benefit from the information they convey. Yet, a systematic evaluations of the contribution of non-lexical markers to model performance, and of whether models' behavior changes significantly in their absence, is currently lacking. This knowledge is both relevant from a theoretical standpoint, but also important to understand how well pretrained models may perform in common application scenarios where casing and punctuation are absent or inconsistent. 
Here, we analyze GPT-2's language modeling behavior in parallel corpora that differ in the presence vs. absence of consistent punctuation and casing. We compute GPT-2's precision and uncertainty in next-token prediction for multiple context sizes, and compare the resulting performance distributions across corpora. We find that absence of non-lexical markers, especially punctuation, increases model uncertainty, and it affects (but does not catastrophically disrupt) GPT-2's precision in next-token prediction. Interestingly, the absence of non-lexical markers prevents the model from benefiting from larger contexts in order to reduce the uncertainty of its predictions. Future work will extend this paradigm to a wider range of models and systematically investigate how features of training text affect both language modeling and downstream predictive performance.

Evaluating the role of non-lexical markers in GPT-2’s language modeling behavior

From COMET to COMES – Can Summary Evaluation Benefit from Translation Evaluation?

Random Text Perturbations Work, but not Always

A Comparative Analysis of Stance Detection Approaches and Datasets

Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset

Determining the origin of a given passage in rabbinic ontologies such as medieval midrash collections is not always straightforward and is often a matter of dispute among scholars, yet it is essential for scholars' understanding of the passage and its relationship to other texts in the rabbinic corpus. 

To help solve this problem, we propose a system for classification of rabbinic literature based on its style, leveraging recently released pretrained Transformer models for Hebrew. By combining our model with a text reuse engine, we demonstrate how our method can be applied to uncover lost material from the Tanhuma-Yelammedenu literature.

Style Classification of Rabbinic Literature for Detection of Lost Midrash Tanhuma Material

Large pre-trained language models have brought remarkable progress in NLP. Pre-training and Fine-tuning have given state-of- art performance across tasks in text processing. Data Augmentation techniques have also helped build state-of-art models on low or zero resource tasks. Many works in the past have attempted at learning a single massively- multilingual machine translation model for zero-shot translation. Although those translation models are producing correct translations, the main challenge is those models are producing the wrong languages for zero-shot translation. This work and its results indicate that prompt conditioned large models do not suffer from off-target language errors i.e. errors arising due to translation to wrong languages. We empirically demonstrate the effectiveness of self-supervised pre-training and data augmentation for zero-shot multi-lingual machine translation.

MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation

Recent studies have reported that neural reasoning improves its accuracy when generating the intermediate reasoning steps. However, it is yet unclear what factors lead to such benefits.
We investigate and factorize the benefit of generating intermediate steps for symbolic reasoning.
Specifically, we decomposed the reasoning strategy regarding step granularity and chaining strategy. 
With a purely symbolic numerical reasoning dataset (e.g., A=1, B=3, C=A+3, C?), we found that the choice of reasoning strategies significantly affects the performance, with the gap becoming even larger as the extrapolation length becomes longer.
Surprisingly, we also found that certain configurations lead to nearly perfect performance, even in the case of length extrapolation.
Our results indicate the importance of further exploring effective strategies for neural reasoning models.

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Exploring Nominal Coercion in Semantic Spaces with Static and Contextualized Word Embeddings.

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