Taiwan

We investigate in this paper how correlations between occupations and gendered-pronouns can be affected and changed by adding negation in bias probes, or changing the grammatical tense of the verbs in the probes. We use a set of simple bias probes in Norwegian and English, and perform 16 different probing analysis, using four Norwegian and four English pre-trained language models. We show that adding negation to probes does not have a considerable effect on the correlations between gendered-pronouns and occupations, supporting other works on negation in language models. We also show that altering the grammatical tense of verbs in bias probes do have some interesting effects on models&#39; behaviours and correlations. We argue that we should take grammatical tense into account when choosing bias probes, and aggregating results across tenses might be a better representation of the existing correlations.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.53/)

IJCNLP-AACL 2022

Exploring the Effects of Negation and Grammatical Tense on Bias Probes

gender-bias in nlp models

bias probes

probing language models

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

We investigate in this paper how correlations between occupations and gendered-pronouns can be affected and changed by adding negation in bias probes, or changing the grammatical tense of the verbs in the probes. We use a set of simple bias probes in Norwegian and English, and perform 16 different probing analysis, using four Norwegian and four English pre-trained language models. We show that adding negation to probes does not have a considerable effect on the correlations between gendered-pronouns and occupations, supporting other works on negation in language models. We also show that altering the grammatical tense of verbs in bias probes do have some interesting effects on models' behaviours and correlations. We argue that we should take grammatical tense into account when choosing bias probes, and aggregating results across tenses might be a better representation of the existing correlations.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.53/)

poster

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

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). 

Being able to train Named Entity Recognition (NER) models for emerging topics is crucial for many real-world applications especially in the medical domain where new topics are continuously evolving out of the scope of existing models and datasets. For a realistic evaluation setup, we introduce a novel COVID-19 news NER dataset (COVIDNEWS-NER) and release 3000 entries of hand annotated strongly labelled sentences and 13000 auto-generated weakly labelled sentences. Besides the dataset, we propose CONTROSTER, a recipe to strategically combine weak and strong labels in improving NER in an emerging topic through transfer learning. We show the effectiveness of CONTROSTER on COVIDNEWS-NER while providing analysis on combining weak and strong labels for training. Our key findings are: (1) Using weak data to formulate an initial backbone before tuning on strong data outperforms methods trained on only strong or weak data. (2) A combination of out-of-domain and in-domain weak label training is crucial and can overcome saturation when being training on weak labels from a single source.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.60/)

How to tackle an emerging topic? Combining strong and weak labels for Covid news NER

Sentence acceptability judgment assesses to what degree a sentence is acceptable to native speakers of the language. Most unsupervised prediction approaches rely on a language model to obtain the likelihood of a sentence that reflects acceptability. However, two problems exist: first, low-frequency words would have a significant negative impact on the sentence likelihood derived from the language model; second, when it comes to multiple domains, the language model needs to be trained on domain-specific text for domain adaptation. To address both problems, we propose a simple method that substitutes Part-of-Speech (POS) tags for low-frequency words in sentences used for continual training of masked language models. Experimental results show that our word-tag-hybrid BERT model brings improvement on both a sentence acceptability benchmark and a cross-domain sentence acceptability evaluation corpus. Furthermore, our annotated cross-domain sentence acceptability evaluation corpus would benefit future research.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.25/)

A Simple Yet Effective Hybrid Pre-trained Language Model for Unsupervised Sentence Acceptability Prediction

Nepali is a low-resource language with more than 40 million speakers worldwide. It is written in Devnagari script and has rich semantics and complex grammatical structure. To this date, multilingual models such as Multilingual BERT, XLM, and XLM-RoBERTa haven’t been able to achieve promising results in Nepali NLP
tasks, and there does not exist any such a large-scale monolingual corpus. This study presents NepBERTa, a BERT-based Natural Language Understanding (NLU) model trained on the most extensive monolingual Nepali corpus ever. We collected a dataset of 0.8B words from 36 different popular news sites in Nepal and introduced the model. This data set is 3 folds times larger than the previous publicly available corpus. We evaluated the performance of NepBERTa in multiple Nepali-specific NLP tasks, including Named-Entity Recognition, Content Classification, POS Tagging, and Categorical Pair Similarity. We also introduce two different datasets for two new downstream tasks and benchmark four diverse NLU tasks altogether. We bring all these four tasks under the first-ever Nepali Language Understanding Evaluation (Nep-gLUE) benchmark. We will make Nep-gLUE along with the pre-trained model
and data sets publicly available for research.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.34/)

NepBERTa: Nepali Language Model Trained in a Large Corpus

Language models are pre-trained using large corpora of generic data like book corpus, common crawl and Wikipedia, which is essential for the model to understand the linguistic characteristics of the language. New studies suggest using Domain Adaptive Pre-training (DAPT) and Task-Adaptive Pre-training (TAPT) as an intermediate step before the final finetuning task. This step helps cover the target domain vocabulary and improves the model performance on the downstream task. In this work, we study the impact of training only the embedding layer on the model’s performance during TAPT and task-specific finetuning. Based on our study, we propose a simple approach to make the intermediate step of TAPT for BERT-based models more efficient by performing selective pretraining of BERT layers. We show that training only the BERT embedding layer during TAPT is sufficient to adapt to the vocabulary of the target domain and achieve comparable performance. Our approach is computationally efficient, with 78% fewer parameters trained during TAPT. The proposed embedding layer finetuning approach can also be an efficient domain adaptation technique.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.39/)

Towards Simple and Efficient Task-Adaptive Pre-training for Text Classification

Polysynthetic languages present a challenge for morphological analysis due to the complexity of their words and the lack of high-quality annotated datasets needed to build and/or evaluate computational models. The contribution of this work is twofold. First, using linguists’ help, we generate and contribute high-quality annotated data for two low-resource polysynthetic languages for two tasks: morphological segmentation and part-of-speech (POS) tagging. Second, we present the results of state-of-the-art unsupervised approaches for these two tasks on Adyghe and Inuktitut. Our findings show that for these polysynthetic languages, using linguistic priors helps the task of morphological segmentation and that using stems rather than words as the core unit of abstraction leads to superior performance on POS tagging.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.41/)

Towards Unsupervised Morphological Analysis of Polysynthetic Languages

A simple machine learning model of pluralisation as a linear regression problem minimising a p-adic metric substantially outperforms even the most robust of Euclidean-space regressors on languages in the Indo-European, Austronesian, Trans New-Guinea, Sino-Tibetan, Nilo-Saharan, Oto-Meanguean and Atlantic-Congo language families. There is insufficient evidence to support modelling distinct noun declensions as a p-adic neighbourhood even in Indo-European languages.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.4/)

Number Theory Meets Linguistics: Modelling Noun Morphology Across 1497 Languages Using 2-adic Metrics

Lifelong language learning seeks to have models continuously learn multiple tasks in a sequential order without suffering from catastrophic forgetting. State-of-the-art approaches rely on sparse experience replay as the primary approach to prevent forgetting. Experience replay usually adopts sampling methods for the memory population; however, the effect of the chosen sampling strategy on model performance has not yet been studied. In this paper, we investigate how relevant the selective memory population is in the lifelong learning process of text classification and question-answering tasks. We found that methods that randomly store a uniform number of samples from the entire data stream lead to high performances, especially for low memory size, which is consistent with computer vision studies.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.20/)

How Relevant is Selective Memory Population in Lifelong Language Learning?

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.31/)

Performance-Efficiency Trade-Offs in Adapting Language Models to Text Classification Tasks

Existing subword segmenters are either 1) frequency-based without semantics information or 2) neural-based but trained on parallel corpora. To address this, we present BERTSeg, an unsupervised neural subword segmenter for neural machine translation, which utilizes the contextualized semantic embeddings of words from characterBERT and maximizes the generation probability of subword segmentations. Furthermore, we propose a generation probability-based regularization method that enables BERTSeg to produce multiple segmentations for one word to improve the robustness of neural machine translation. Experimental results show that BERTSeg with regularization achieves up to 8 BLEU points improvement in 9 translation directions on ALT, IWSLT15 Vi->En, WMT16 Ro->En, and WMT15 Fi->En datasets compared with BPE. In addition, BERTSeg is efficient, needing up to 5 minutes for training.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.12/)

BERTSeg: BERT Based Unsupervised Subword Segmentation for Neural Machine Translation

Machine translation traditionally refers to translating from a single source language into a single target language. In recent years, the field has moved towards large neural models translating from or into many languages. As the input and output languages vary, the model must be correctly cued to translate into the correct target language. This is typically done by prefixing language tokens onto the source or target sequence. A single token’s content can denote the source language, target language, or language pair. The location and content of the prefix varies and many approaches exist without much justification towards one method or another. As guidance to researchers and directions for future work, we present a series of comprehensive experiments that show how the positioning and type of a target language prefix token affects translation performance. We show that source-side prefixes consistently improve performance. Further, we find that best language token content varies dependent on the supported language set.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.19/)

Exploring the Effects of Negation and Grammatical Tense on Bias Probes

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How to tackle an emerging topic? Combining strong and weak labels for Covid news NER

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