United States

The tasks of lexical complexity prediction (LCP) and complex word identification (CWI) commonly presuppose that difficult to understand words are shared by the target population. Meanwhile, personalization methods have also been proposed to adapt models to individual needs. We verify that a recent Japanese LCP dataset is representative of its target population by partially replicating the annotation. By another reannotation we show that native Chinese speakers perceive the complexity differently due to Sino-Japanese vocabulary. To explore the possibilities of personalization, we compare competitive baselines trained on the group mean ratings and individual ratings in terms of performance for an individual. We show that the model trained on a group mean performs similarly to an individual model in the CWI task, while achieving good LCP performance for an individual is difficult. We also experiment with adapting a finetuned BERT model, which results only in marginal improvements across all settings.

EMNLP 2024

Difficult for Whom? A Study of Japanese Lexical Complexity

lexical complexity

lexical simplification

personalization

japanese

complex word identification

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ACL 2023

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EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

Vision-language models (VLMs) have recently shown promising results in traditional downstream tasks. The capability of VLMs to ``think'' from a first-person perspective, a crucial attribute for advancing autonomous agents and robotics, remains largely unexplored. To bridge this research gap, we introduce EgoThink, a novel visual question-answering benchmark that encompasses six core capabilities with twelve detailed dimensions. The benchmark is constructed using selected clips from egocentric videos, with manually annotated question-answer pairs containing first-person information. To comprehensively assess VLMs, we evaluate twenty-one popular VLMs on EgoThink. Moreover, given the open-ended format of the answers, we use GPT-4 as the automatic judge to compute single-answer grading. Experimental results indicate that although GPT-4V leads in numerous dimensions, all evaluated VLMs still possess considerable potential for improvement in first-person perspective tasks.

EgoThink: Evaluating First-Person Perspective Thinking Capability of Vision-Language Models

Large Language Models (LLMs) for Bangla text summarization condense texts while pre serving key information by leveraging ad vanced Natural Language Processing (NLP) techniques. In this study, we used two popular Bangla news summarization datasets through the evaluation of 5 LLMs as well as human evaluation. we made two key observations. First, we found that GPT-4 with zero-shot model settings performs well in Bangla news summarization. Secondly, previous research has been constrained by low-quality references, resulting in an underestimation of human per formance and diminished few-shot capabilities. To more accurately evaluate LLMs, we per formed human assessments using high-quality summaries created by student writers. De spite notable stylistic differences, including the extent of paraphrasing, LLM-generated sum maries were found to be comparable to those written by humans. Our model was assessed both qualitatively and quantitatively, and com parisons with other published results showed significant improvements in human evaluation scores due to the LLM techniques.

Evaluating Large Language Models for Summarizing Bangla Texts

Torwali [ISO 639-3: trw] is an endangered and indigenous language spoken in North of Pakistan. It is a low-resource language writ-ten in RTL Perso-Arabic script. This paper discusses the challenges and approaches in processing of Torwali with various NLP tech-niques to develop tools and resources. This work contributes towards morphological analysis, word segmentation, POS tagging and transliteration of Torwali. This work, on which this paper is based, can be used as a resource for other lexically similar endan-gered languages of northern Pakistan and will help to improve the digital presence of Tor-wali language and will safeguard it against endangerment

Developing Language Technology and NLP tools for endangered languages: Torwali

This study explores Pre-trained Language Models (PLMs) for Arabic mental health question answering using the novel MentalQA dataset. We establish a baseline for future research and compare PLMs to classical models. Fine-tuned PLMs outperform classical models, with MARBERT achieving the best results (0.89 F1-score). Few-shot learning with GPT models also shows promise. This work highlights PLMs' potential for Arabic mental health applications while identifying areas for further development.

A Comparative Study of Using Pre-trained Language Models for Mental Healthcare Q&A Classification in Arabic

This study focuses on developing a sentence-level automatic speech segmentation system for Amharic. Two approaches were explored. The first approach utilized an automatic tool for segmenting and labeling Amharic speech data, creating an acoustic model through HMM modeling. The system's segmentation was refined using forced alignment AdaBoost techniques. In the second approach, prosodic features were extracted directly from the speech waveform, and statistical methods including AdaBoost were employed. Additionally, LSTM and Bi-LSTM models were utilized, achieving impressive accuracies of 94.62% and 95.23%, respectively. These approaches contribute to advancing automatic speech segmentation for Amharic, promising improved accuracy and efficiency.

Enhancing Amharic Sentence Segmentation with Prosodic Features and Neural Network Models

The rise of video content on social media has led to toxic speech spread, necessitating effec tive moderation. This study addresses detect ing toxic speech in Bangla and Bangla-English videos using multimodal data and deep learn ing techniques. The BanTSS dataset, with 431 videos and 2021 annotated utterances, supports this research. We propose the BanVATLLM framework, a multimodal architecture integrat ing audio, video, and text data. Utilizing ad vanced models like Whisper, MMS, Video MAE, Timesformer, and ChatGPT-3.5, Ban VATLLMshows high accuracy in classifying toxicity, sentiment, and severity, with high F1 scores and Fleiss’ Kappa scores. Results in clude 95.78% F1 and 95.72% accuracy for toxi city, 88.27% F1 and 88.55% accuracy for sever ity, and 84.85% F1 and 83.86% accuracy for sentiment, enhancing detection in low-resource languages.

BanVATLLM and BanTSS: A Multimodal Framework and a Dataset for Detecting Toxic Speech in Bangla and Bangla-English Videos

Large Language Models (LLMs) show promising learning and reasoning abilities. Compared to other NLP tasks, multilingual and multi-label emotion evaluation is under-explored in LLMs. In this paper, we present EthiopicEmotion, a multi-label emotion with intensity dataset for four Ethiopian languages, namely Amharic (amh), Afan Oromo (oro), Somali (som), and Tigrinya (tir). We perform extensive experiments with additional English multi-label emotion data from SemEval 2018: Affect in tweets work. Our evaluation includes zero-shot and in-context learning (ICL) from large language models. The result shows that accurate multi-label emotion classification is still insufficient, even for high-resource languages (English), and there is a large gap between the performance of English and low-resource languages.

EthiopicEmotion: Multi-label Emotion Dataset with Large Language Models Evaluation

NLP is severely hampered by a scarcity of digital resources. This is especially true for Amharic, a language with few resources but a rich morphology. In response, a total of 67,739 Amharic news documents from 8 different categories are gathered from web sources. A baseline document categorization experiment is carried out to validate the usability of the obtained corpora from various domains.

Document Classification for the Under-resourced Amharic Language

African languages are not well-represented in Natural Language Processing (NLP). The main reason is a lack of resources for training models. Low-resource languages, such as Amharic and Ge’ez, cannot benefit from modern NLP methods because of the lack of high-quality datasets. This paper presents AGE, an open-source tripartite alignment of Amharic, Ge’ez, and English parallel dataset. Additionally, we introduced a novel, 1,000 Ge’ez-centered sentences sourced from areas such as news and novels. Furthermore, we developed a model from a multilingual pre-trained language model, which brings 12.29 and 30.66 for English-Ge’ez and Ge’ez to English, respectively, and 9.39 and 12.29 for Amharic-Ge’ez and Ge’ez-Amharic respectively.

AGE: Amharic, Ge’ez and English Parallel Dataset

This study presents a comprehensive, long-term project to explore the effectiveness of various prompting techniques in detecting dialogical mental manipulation. We implement Chain-of-Thought prompting with Zero-Shot and Few-Shot settings on a binary mental manipulation detection task, building upon existing work conducted with Zero-Shot and Few-Shot prompting. Our primary objective is to decipher why certain prompting techniques display superior performance so as to craft a novel framework tailored for the detection of mental manipulation. Preliminary findings suggest that advanced prompting techniques may not be suitable for more complex models if they are not trained through example-based learning.

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