Papers by Sadao Kurohashi
Development of a Japanese Personality Dictionary based on Psychological Methods (2020.lrec-1)
Copied to clipboard
| Challenge: | a new approach to constructing a personality dictionary with psychological evidence is needed . we use abstract terms such as "sociable person" or "kind" to describe ourselves or others . |
| Approach: | They propose a Japanese personality dictionary with weights for Big Five traits . they collect personality words and use word embeddings to construct the dictionary . |
| Outcome: | The proposed approach is the first to have psychological evidence tolerant to NLP standards. |
Constructing a Culinary Interview Dialogue Corpus with Video Conferencing Tool (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing interview dialogue corpora are based on news interviews which serve the purpose of information broadcasting or entertainment. |
| Approach: | They propose an interview dialogue corpus in the culinary domain in which interviewers play an active role to elicit culinary knowledge from the cooking expert. |
| Outcome: | The proposed corpus consists of 308 interview dialogues, each about 13 minutes long, which add up to a total of 69,000 utterances. |
SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Empirical studies show that supervised learning is extremely effective in in-domain datasets and models trained on SuperDialseg can achieve good generalization ability on out-of-domain data. |
| Approach: | They propose a supervised definition of dialogue segmentation points using document-grounded dialogues and a large-scale supervised dataset called SuperDialseg. |
| Outcome: | The proposed model can achieve good generalization ability on out-of-domain data. |
Comprehensive Annotation of Various Types of Temporal Information on the Time Axis (L18-1)
Copied to clipboard
| Challenge: | Existing studies linking event and time information have been conducted to train and evaluate models. |
| Approach: | They propose an annotation scheme that anchors expressions in text to the time axis comprehensively. |
| Outcome: | The proposed scheme can be utilized for integrated information analysis of events, entities and time. |
Is a Knowledge-based Response Engaging?: An Analysis on Knowledge-Grounded Dialogue with Information Source Annotation (2023.acl-srw)
Copied to clipboard
| Challenge: | Currently, most knowledge-grounded dialogue models focus on reflecting given external knowledge. |
| Approach: | They analyze human behavior by annotating utterances in an existing knowledge-grounded dialogue corpus and find that speaker-derived information improves dialogue engagingness. |
| Outcome: | The proposed model cannot include speaker-derived information as often as humans do. |
Explicit Use of Topicality in Dialogue Response Generation (2022.naacl-srw)
Copied to clipboard
| Challenge: | Existing chat dialogue systems only implicitly consider the topic given the context, but not explicitly. |
| Approach: | They propose a dialogue system that responds appropriately following the topic by selecting the entity with the highest “topicality” they define the entity as a noun or compound nouns, and topicality as the degree of speaker awareness directed toward each entity in the dialogue context. |
| Outcome: | The proposed system can follow the topic more than existing systems that only consider the context . |
Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis (P18-1)
Copied to clipboard
| Challenge: | Existing methods for predicate argument structure analysis are difficult and difficult . a Japanese model can detect a zero pronoun and identify a referent of the zero pronominator . |
| Approach: | They propose a model that performs coreference resolution and predicate argument structure analysis simultaneously. |
| Outcome: | The proposed model can improve the performance of the inter-sentential zero anaphora resolution drastically. |
J-CRe3: A Japanese Conversation Dataset for Real-world Reference Resolution (2024.lrec-main)
Copied to clipboard
Nobuhiro Ueda, Hideko Habe, Akishige Yuguchi, Seiya Kawano, Yasutomo Kawanishi, Sadao Kurohashi, Koichiro Yoshino
| Challenge: | Existing studies have ground referential expressions in language to real-world objects for cooperative action generation. |
| Approach: | They propose a Japanese Conversation dataset for real-world reference resolution that ground referential expressions to visual information observed in egocentric views. |
| Outcome: | The proposed dataset contains egocentric video and dialogue audio of real-world conversations between two people acting as a master and assistant robot at home. |
An Empirical Study of Synthetic Data Generation for Implicit Discourse Relation Recognition (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to recognize implicit discourse relations are limited by the lack of training data. |
| Approach: | They propose a method to generate synthetic data for IDRR using a large language model . they extract confused discourse relation pairs based on false negative rate and use two-stage prompting to generate effective synthetic data. |
| Outcome: | The proposed method achieves state-of-the-art macro-F1 performance without sacrificing micro-F1. |
Video-guided Machine Translation with Spatial Hierarchical Attention Network (2021.acl-srw)
Copied to clipboard
| Challenge: | Existing studies use pretrained motion detection models as verb sense ambiguity representations to solve the verb sense problem. |
| Approach: | They propose to use video contents as auxiliary information to address the word sense ambiguity problem in machine translation. |
| Outcome: | Experiments on the VATEX dataset show that the proposed system achieves 35.86 BLEU-4 score, which is 0.51 score higher than the single model of the SOTA method. |
Shrinking Japanese Morphological Analyzers With Neural Networks and Semi-supervised Learning (N19-1)
Copied to clipboard
| Challenge: | Modern neural morphological analyzers consume gigabytes of memory. |
| Approach: | They propose a method which uses unigram character embeddings to train a model on labels produced by a state-of-the-art analyzer. |
| Outcome: | The proposed model outperforms dictionary-based methods in Japanese and Chinese . it uses less than 15 megabytes of space and is much smaller than the dictionary- based one . |
Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent large language models like GPT-4 have demonstrated astonishing zero-shot capabilities in general domain tasks, but they often generate content with hallucinations in specific domains such as Chinese law. |
| Approach: | They propose a framework for adapting large language models (LLMs) to Chinese legal domains by reformulating generation as an adapt-retrieve-revise process. |
| Outcome: | The proposed framework outperforms existing models in the Chinese legal domain by +33.6 points in the zero-shot setting. |
Textual Enhanced Contrastive Learning for Solving Math Word Problems (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies show that current models rely on shallow heuristics to predict solutions . a textual Enhanced Contrastive Learning framework enforces the models to distinguish semantically similar examples while holding different mathematical logic. |
| Approach: | They propose a textual Enhanced Contrastive Learning framework which enforces models to distinguish semantically similar examples while holding different mathematical logic. |
| Outcome: | The proposed framework improves on benchmark and challenge datasets in English and Chinese. |
A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification (C18-1)
Copied to clipboard
| Challenge: | Existing studies on implicit discourse relation classification have shown success using feedforward networks and convolutional neural networks. |
| Approach: | They propose to augment input text with external knowledge and context and adopt a neural network model that can effectively handle the augmented text. |
| Outcome: | The proposed model outperforms existing models on implicit discourse relation classification. |
ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing weakly-supervised methods for solving math word problems are expensive and time-consuming. |
| Approach: | They propose a weakly-supervised approach to solve math word problems . they propose 'comsearch' algorithm which compresses the search space by excluding mathematically equivalent equations. |
| Outcome: | The proposed algorithm can compress the search space by excluding mathematically equivalent equations. |
Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs (2026.acl-long)
Copied to clipboard
Yihua Zhu, Qianying Liu, Jiaxin Wang, Fei Cheng, Chaoran Liu, Akiko Aizawa, Sadao Kurohashi, Hidetoshi Shimodaira
| Challenge: | Autoregressive LLMs perform well on relational tasks that require linking entities via relational words, but it is unclear whether they learn the logical semantics of such relations or whether left-to-right order bias is involved. |
| Approach: | They propose a framework that generates text from symmetric/inverse triples and trains autoregressive models from scratch. |
| Outcome: | The proposed framework generates text from symmetric/inverse triples, trains autoregressive models from scratch, and evaluates memorization, logical inference, and in-context generalization to unseen entities. |
Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction (D19-60)
Copied to clipboard
| Challenge: | Typical event sequences are important class of commonsense knowledge . previous work in event prediction uses sequence-to-sequence models . however, what can happen after a given event is usually diverse . |
| Approach: | They propose to incorporate a conditional variational autoencoder into seq2seq for its ability to represent diverse next events as a probabilistic distribution. |
| Outcome: | The proposed model outperforms deterministic models in terms of precision and recall . the proposed model is based on a conditional variational autoencoder . |
Native-like Expression Identification by Contrasting Native and Proficient Second Language Speakers (2020.coling-main)
Copied to clipboard
| Challenge: | a novel task of native-like expression identification is proposed by contrasting texts written by native speakers and those by proficient second language speakers. |
| Approach: | They propose a task of native-like expression identification by contrasting texts written by native speakers and those by proficient second language speakers. |
| Outcome: | The proposed method uncovers linguistically interesting usages distinctive of native speech. |
Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems (2022.aacl-short)
Copied to clipboard
| Challenge: | Existing methods to solve Math Word Problems rely on human annotation . empirical results suggest that our method universally improves the performance on single-unknown and multiple-un unknown benchmarks. |
| Approach: | They propose a controlled equation generation solver by leveraging a set of control codes to guide the model to consider certain reasoning logic and decode the corresponding equations expressions transformed from the human reference. |
| Outcome: | The proposed method improves performance on single-unknown and multiple-un unknown benchmarks with 13.2% accuracy on the challenging multiple-unequal datasets. |
Identifying Source Language Expressions for Pre-editing in Machine Translation (2024.lrec-main)
Copied to clipboard
| Challenge: | MT-mediated communication can benefit from pre-editing source language texts to ensure accurate transmission of intended meaning in the target language. |
| Approach: | They hypothesize that such expressions tend to be distinctive features of texts originally written in the source language rather than translations generated from the target language into the source languages. |
| Outcome: | The proposed method identified characteristic expressions of the native language despite the noise and inherent nuances of the task. |
Knowledge-Enriched Two-Layered Attention Network for Sentiment Analysis (N18-2)
Copied to clipboard
| Challenge: | Existing sentiment analysis systems are prone to word shortening, exaggeration, lack of grammar and appropriate punctuation. |
| Approach: | They propose a two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis using the Knowledge Graph Embedding generated using the WordNet. |
| Outcome: | The proposed model outperforms the state-of-the-art system on the benchmark dataset of SemEval 2017 Task 5 by 1.7 and 3.7 points respectively. |
Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have aimed to refine their capacity to accurately follow human instructions and navigate intricate scenarios. |
| Approach: | They propose a method that uses a set of instructions to translate English into Japanese and then generates Japanese instruction data using GPT-4. |
| Outcome: | The proposed method outperforms Japanese-Alpaca models in the evaluation benchmarks without human references. |
Flexible Visual Grounding (2022.acl-srw)
Copied to clipboard
| Challenge: | Existing visual grounding datasets require queries to be answerable, but in multimedia data, many entities cannot be grounded to the image, resulting in unanswerable visual ground. |
| Approach: | They propose a method to ground to a pseudo image region for unanswerable queries . they add a query that cannot be grounded to the image and train it to ground . |
| Outcome: | The proposed model can handle answerable and unanswerable visual grounding with high accuracy on the proposed datasets. |
What Language Do Non-English-Centric Large Language Models Think in? (2025.findings-acl)
Copied to clipboard
Chengzhi Zhong, Qianying Liu, Fei Cheng, Junfeng Jiang, Zhen Wan, Chenhui Chu, Yugo Murawaki, Sadao Kurohashi
| Challenge: | Despite their robust performance in English, these models often exhibit reduced proficiency in non-English languages, and their outputs may reflect an inherent bias toward English-centric perspectives. |
| Approach: | They categorize non-English-centric large language models into two groups: CPMs and BLMs, which are pre-trained on a balanced mix of multiple languages from scratch. |
| Outcome: | The proposed models exhibit a pronounced internal preference for English tokens when projected into the vocabulary space. |
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods ignore the intrinsic noise of distant supervision during the pre-training stage. |
| Approach: | They propose a weighted contrastive learning method that explicitly reduces noise . they leverage supervised data to estimate reliability and reduce noise compared to non-weighted baselines . |
| Outcome: | The proposed method reduces the noise of distant supervision and estimates reliability of pre-training instances. |
JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation (2020.lrec-1)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) requires large parallel corpora for training robust and high quality models. |
| Approach: | They propose a Japanese-specific sequence to sequence pre-training alternative to MASS for NMT . they use Japanese as the source or target language to train their models . |
| Outcome: | The proposed approach can give competitive results over MASS and BRSS, and significantly surpass the individual methods. |
Improving Commonsense Contingent Reasoning by Pseudo-data and Its Application to the Related Tasks (2022.coling-1)
Copied to clipboard
| Challenge: | Contingent reasoning is one of the essential abilities in natural language understanding . despite advances in deep learning, the task of contingent reasoning is still difficult for computers . |
| Approach: | They propose to generate large-scale pseudo-problems and incorporate them into training . they also investigate the generality of contingent knowledge through quantitative evaluation . |
| Outcome: | The proposed method is able to evaluate the generality of contingent knowledge through transfer learning. |
Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing RE models are incapable of handling implicit expressions and long-tail relation types due to language complexity and data sparsity. |
| Approach: | They propose a method to enhance relation extraction using k nearest neighbors (kNN-RE) kNN is a nearest-neighbor search tool that allows the model to consult training relations at test time . |
| Outcome: | The proposed model outperforms the best model to date on ACE05, SciERC, and Wiki80 datasets and outperformed the best on i2b2 and Wik80 dataset. |
Language Lives in Sparse Dimensions: Toward Interpretable and Efficient Multilingual Control for Large Language Models (2026.eacl-long)
Copied to clipboard
| Challenge: | Prior studies show that large language models map multilingual content into English-aligned representations at intermediate layers before projecting them back into target-language token spaces in the later layers. |
| Approach: | They propose a method to identify and manipulate dimensions that are sparse and sparsity-based . they propose to use as few as 50 sentences of either parallel or monolingual data to manipulate these dimensions . |
| Outcome: | Experiments on a multilingual generation control task show the interpretability of these dimensions. |
7 Points to Tsinghua but 10 Points to ? Assessing Large Language Models in Agentic Multilingual National Bias (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, but studies on risks associated with cross biases are limited to immediate context preferences. |
| Approach: | They investigate multilingual bias in state-of-the-art Large Language Models by analyzing their responses to decision-making tasks across multiple languages. |
| Outcome: | The proposed model can provide personalized advice across university applications, travel, and relocation scenarios. |
Towards a Versatile Medical-Annotation Guideline Feasible Without Heavy Medical Knowledge: Starting From Critical Lung Diseases (2020.lrec-1)
Copied to clipboard
| Challenge: | Current annotation policies for medical corpora are not standardized across clinical texts of different types. |
| Approach: | They propose to annotate medical records of various types using a named entity recognition (NER) task. |
| Outcome: | The proposed annotation scheme is applicable to large-scale clinical NLP projects. |
Abstractive Multi-Video Captioning: Benchmark Dataset Construction and Extensive Evaluation (2024.lrec-main)
Copied to clipboard
| Challenge: | Abstractive multi-video captioning focuses on abstracting multiple videos with natural language. |
| Approach: | They propose a task that generates an abstract caption of shared video content . they propose end-to-end and cascade approaches to abstractive multi-video captioning . |
| Outcome: | The proposed task generates an abstract caption of shared content in a video group containing multiple videos. |
Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data (N19-1)
Copied to clipboard
| Challenge: | Argument compatibility is a linguistic condition that is often used in event coreference resolution systems. |
| Approach: | They propose a transfer learning framework that uses unlabeled data to learn argument compatibility of event mentions. |
| Outcome: | The proposed model improves the performance of the overall event coreference model on the English dataset. |
SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models (2025.acl-long)
Copied to clipboard
Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi
| Challenge: | SIQ quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Approach: | They propose a human cognition-inspired evaluation pipeline for voice understanding large language models (LLM_Voice) that quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Outcome: | The proposed framework quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models, identifies annotation errors in existing benchmarks, and detects hallucinations in LLM_Voice. |
Improving Crowdsourcing-Based Annotation of Japanese Discourse Relations (L18-1)
Copied to clipboard
| Challenge: | Discourse parsing is an important task in natural language processing, but few languages have corpora annotated with discourse relations . crowdsourcing-based annotations are of poor quality and require expensive and time-consuming . et al. (2009) evaluated the quality of annotations using expert annotations. |
| Approach: | They construct a Japanese corpus with discourse annotations through crowdsourcing . they propose improvement techniques based on language tests . |
| Outcome: | The proposed methods improve the quality of the annotations, and will make them publicly available. |
Pre-training via Leveraging Assisting Languages for Neural Machine Translation (2020.acl-srw)
Copied to clipboard
| Challenge: | Sequence-to-sequence (S2S) pre-training with large monolingual data is not always available for the languages of interest (LOI). |
| Approach: | They propose to use monolingual corpora of other languages to complement the scarce monolingual LOI by script mapping (Chinese to Japanese) . Using only Chinese and French monolinguals, they improve Japanese-English translation quality by up to 8.5 BLEU in low-resource scenarios. |
| Outcome: | The proposed approach improves Japanese-English translation quality by up to 8.5 BLEU in low-resource scenarios. |
Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation (2020.lrec-1)
Copied to clipboard
| Challenge: | Lectures translation is a case of spoken language translation and there is nil available corpus for this purpose. |
| Approach: | They propose a framework for mining a parallel corpus from publicly available lectures at Coursera . they use machine translation and cosine similarity over continuous-space sentence representations to determine sentence alignments . |
| Outcome: | The proposed framework improves translation performance when used with out-of-domain parallel corpora . it also addresses noise in the mined data, and creates high-quality evaluation splits . |
Japanese Zero Anaphora Resolution Can Benefit from Parallel Texts Through Neural Transfer Learning (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Using a pretraining model, we find that the performance of Japanese zero anaphora resolution (ZAR) is improved by using machine translation. |
| Approach: | They propose to inject machine translation as an intermediate task between pretraining and ZAR by injecting machine translation into a pretrained BERT model and injecting it into MT. |
| Outcome: | The proposed framework shows that Japanese zero anaphora resolution (ZAR) can be improved by transfer learning from machine translation (MT). |
Causal Tree Extraction from Medical Case Reports: A Novel Task for Experts-like Text Comprehension (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to extract causal relationships from medical case reports are insufficient for capturing causal relationships of an entire case. |
| Approach: | They propose a task that generates a causal tree with the primary disease as the root and extracts causal relationships from a medical case report. |
| Outcome: | The proposed method outperforms the baseline method by 20.2 points in the human evaluation and introduces evaluation metrics that reflect clinician preferences. |
VISA: An Ambiguous Subtitles Dataset for Visual Scene-aware Machine Translation (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing multimodal machine translation datasets contain images and video captions or general subtitles which rarely contain linguistic ambiguity. |
| Approach: | They propose a dataset that consists of Japanese-English parallel sentence pairs and corresponding video clips. |
| Outcome: | The proposed dataset is challenging for the latest MMT system and can facilitate MMT research. |
Improving Event Duration Question Answering by Leveraging Existing Temporal Information Extraction Data (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing tasks that require knowledge about event duration require limited duration data . a two-stage fine-tuning approach might fail due to discrepancy between task and duration data. |
| Approach: | They propose to recast existing event duration classification task to a question answering task similar to McTACO. |
| Outcome: | The proposed model achieves a 13% Exact Match score improvement over baseline on the McTACO duration question answering task. |
Building a Japanese Typo Dataset from Wikipedia’s Revision History (2020.acl-srw)
Copied to clipboard
| Challenge: | Typographical errors (typos) also occur in user generated content (UGC). |
| Approach: | They extract over half a million Japanese typo–correction pairs from Wikipedia’s revision history and combine character-based extraction rules, morphological analyzers to guess readings, and various filtering methods to address these challenges. |
| Outcome: | The proposed dataset extracts over half a million typo–correction pairs from Wikipedia’s revision history. |
Extractive Summarization Considering Discourse and Coreference Relations based on Heterogeneous Graph (2021.eacl-main)
Copied to clipboard
| Challenge: | Abstractive summarization aims to select salient text spans (mostly sentences) from the input document. |
| Approach: | They propose a heterogeneous graph based model that incorporates both discourse and coreference relations between text spans of different granularity. |
| Outcome: | The proposed model is efficient and factually reliable on a benchmark summarization dataset. |
Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion (C18-1)
Copied to clipboard
| Challenge: | Existing efforts to build commonsense knowledge bases are expensive and lack quantity and quality between languages. |
| Approach: | They propose to project English commonsense knowledge into Japanese and Chinese with high precision. |
| Outcome: | The proposed method achieves top-10 accuracy on the crowdsourced English–Japanese benchmark and 18,747 facts of accurate Japanese commonsense within a very short period. |
Domain Transferable Semantic Frames for Expert Interview Dialogues (2024.lrec-main)
Copied to clipboard
| Challenge: | a dataset of interview dialogues with experts in the domains of culinary and gardening domains is used to structure domain-specific knowledge in expert interviews. |
| Approach: | They analyze interview dialogues with experts in the culinary and gardening domains to understand their domain knowledge structure. |
| Outcome: | The proposed framework is effective in eliciting critical skills in domains, the authors show . they use domain-agnostic labels to identify domain-specific knowledge in interviews . |
Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis (P18-1)
Copied to clipboard
| Challenge: | Japanese predicate-argument structure analysis involves zero anaphora resolution . state-of-the-art models for PAS analysis achieve an accuracy of around 50% for zero pronouns . |
| Approach: | They propose a Japanese PAS analysis model based on semi-supervised adversarial training with a raw corpus. |
| Outcome: | The proposed model outperforms existing models for Japanese PAS analysis . the model is based on semi-supervised adversarial training with a raw corpus . |
Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model (2021.naacl-main)
Copied to clipboard
| Challenge: | linguistic steganography is the practice of concealing a secret message in some cover data such that an eavesdropper is not even aware of the existence of the secret message. |
| Approach: | They propose to use edit-based linguistic steganography to generate genuine-looking texts by using a masked language model that eliminates painstaking rule construction and has a high payload capacity. |
| Outcome: | The proposed method eliminates painstaking rule construction and has a high payload capacity for an edit-based model. |
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models with independent classifiers for each TLINK category hinder from using the whole data. |
| Approach: | They propose a temporal relation classification model that manages dynamic event representations across multiple TLINKs using multi-task learning to leverage the full size of data. |
| Outcome: | The proposed model outperforms state-of-the-art models and two strong transfer learning baselines on English and Japanese data. |
Minimally Supervised Learning of Affective Events Using Discourse Relations (D19-1)
Copied to clipboard
| Challenge: | Existing methods for learning affective events that trigger positive or negative sentiment are difficult because of the unbounded combinatorial nature of language. |
| Approach: | They propose to propagate affective polarity using discourse relations using a small seed lexicon and large raw corpus. |
| Outcome: | The proposed method learns affective events effectively without manually labeled data, and improves supervised learning when labeles are small. |
Lightweight Cross-Lingual Sentence Representation Learning (2021.acl-long)
Copied to clipboard
| Challenge: | Existing models for learning fixed-dimensional cross-lingual sentence representations are impractical due to memory limitations. |
| Approach: | They propose a lightweight dual-transformer architecture with just 2 layers for generating memory-efficient cross-lingual sentence representations. |
| Outcome: | The proposed model improves performance on training tasks and improves memory efficiency. |
ARKitSceneRefer: Text-based Localization of Small Objects in Diverse Real-World 3D Indoor Scenes (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing datasets for 3D referring expression comprehension cover large objects and small objects, such as cooking tools and office supplies. |
| Approach: | They propose a 3D referring expression comprehension dataset that uses 3D scenes to ground text representations onto objects in 3D environments. |
| Outcome: | The proposed dataset covers 15k objects of 1,605 indoor scenes and is significantly larger than existing datasets. |
Juman++: A Morphological Analysis Toolkit for Scriptio Continua (D18-2)
Copied to clipboard
| Challenge: | a morphological analyzer is useful for languages without natural word boundaries, but it is difficult to improve it without creating costly annotations. |
| Approach: | They propose a toolkit for developing morphological analyzers for languages without natural word boundaries using lattices and neural nets. |
| Outcome: | The proposed morphological analyzer of Japanese achieves new SOTA on Jumandic-based corpora while being 250 times faster than the previous one. |
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction (2020.findings-emnlp)
Copied to clipboard
Ranran Haoran Zhang, Qianying Liu, Aysa Xuemo Fan, Heng Ji, Daojian Zeng, Fei Cheng, Daisuke Kawahara, Sadao Kurohashi
| Challenge: | Existing methods to extract relation triplets from plain text introduce exposure bias . prior work has focused on pipeline methods that ignore intrinsic interactions between subtasks and propagate classification errors through the tasks. |
| Approach: | They propose a model that reduces the decoding length to three within a triplet and removes the order among triplets. |
| Outcome: | The proposed model overfits to both datasets while showing better generalization. |
JaMIE: A Pipeline Japanese Medical Information Extraction System with Novel Relation Annotation (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing tools for analyzing medical information extraction are limited . empirical results show satisfactory analyzing performance . |
| Approach: | They propose a relation annotation schema for investigating medical and temporal relations in Japanese medical reports. |
| Outcome: | The proposed schema shows that it performs better than existing models and is feasible for high-accuracy applications. |
GPT-RE: In-context Learning for Relation Extraction using Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to in-context learning (ICL) are lacking in relation extraction (RE) . emergence of large language models (LLMs) such as GPT-3 represents a significant advancement in natural language processing. |
| Approach: | They propose to incorporate task-aware representations into demonstration retrieval and enrich the demonstrations with gold label-induced reasoning logic. |
| Outcome: | The proposed model achieves SOTA and competitive performances on the Semeval and SciERC datasets. |
Overview of the 6th Workshop on Asian Translation (D19-52)
Copied to clipboard
Toshiaki Nakazawa, Nobushige Doi, Shohei Higashiyama, Chenchen Ding, Raj Dabre, Hideya Mino, Isao Goto, Win Pa Pa, Anoop Kunchukuttan, Yusuke Oda, Shantipriya Parida, Ondřej Bojar, Sadao Kurohashi
| Challenge: | The 6th workshop on Asian translation (WAT2019) was held in hong kong, hongkong, and hong kong. |
| Approach: | They present the results of the shared tasks from the 6th workshop on Asian translation (WAT2019) 25 teams participated in the shared task and 10 research paper submissions were accepted . |
| Outcome: | The results of the 6th workshop on Asian translation (WAT2019) include JaEn, JaZh scientific paper translation subtasks, Ja'En, ja'Ko, Ja’En patent translation sub tasks, Hi'En and My'En patent subtask and Ru'Ja news commentary translation task. |
MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting (2023.acl-short)
Copied to clipboard
| Challenge: | Recent studies have focused on using a single external tool to solve a problem with large language models and have not addressed different problems together. |
| Approach: | They propose a framework that leverages chain-of-thought prompting to incorporate multiple external tools into the reasoning process. |
| Outcome: | The proposed framework outperforms baselines and achieves state-of-the-art performance on a task that requires both numerical reasoning and domain-specific knowledge. |
MELD-ST: An Emotion-aware Speech Translation Dataset (2024.findings-acl)
Copied to clipboard
Sirou Chen, Sakiko Yahata, Shuichiro Shimizu, Zhengdong Yang, Yihang Li, Chenhui Chu, Sadao Kurohashi
| Challenge: | Emotion plays a crucial role in human conversation. |
| Approach: | They present a MELD-ST dataset for the emotion-aware speech translation task . they show that fine-tuning with emotion labels can enhance translation performance . |
| Outcome: | The proposed dataset shows that fine tuning with emotion labels can improve translation performance in some settings. |
Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing studies on the performance of BERT for implicit discourse relation classification have not been conducted. |
| Approach: | They propose to apply BERT to implicit discourse relation classification by performing additional pre-training on text tailored to discourse relations. |
| Outcome: | The proposed methods outperform previous state-of-the-art models in many tasks. |
KWJA: A Unified Japanese Analyzer Based on Foundation Models (2023.acl-demo)
Copied to clipboard
Nobuhiro Ueda, Kazumasa Omura, Takashi Kodama, Hirokazu Kiyomaru, Yugo Murawaki, Daisuke Kawahara, Sadao Kurohashi
| Challenge: | KWJA supports a wide range of tasks including typo correction, word segmentation, word normalization, named entity recognition, dependency parsing, PAS analysis, bridging reference resolution, coreference resolution, and discourse relation analysis. |
| Approach: | They propose to build a Japanese text analyzer based on foundation models that performs a wide range of tasks. |
| Outcome: | The proposed model performs better in a multi-task manner than other analyzers with specialized models. |
Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis (D19-58)
Copied to clipboard
| Challenge: | a lack of gold datasets and knowledge about PAS analysis makes it difficult to create accurate PAS analyses. |
| Approach: | They construct a Japanese blog-QA dataset and a reading comprehension QA dataset using crowdsourcing. |
| Outcome: | The proposed method is most effective, pre-training model to acquire domain knowledge and fine-tuning model based on PAS-QA dataset. |
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation? (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods to improve pre-training for many-to-many neural machine translation use manual cleaning of bilingual dictionaries, which are unavailable for most language pairs. |
| Approach: | They propose a word-level contrastive objective to leverage word alignments for many-to-many neural machine translation (NMT) Empirical results show that this leads to 0.8 BLEU gains for several language pairs. |
| Outcome: | Empirical results show that the proposed objective leads to 0.8 BLEU gains for several language pairs. |
Acquiring Social Knowledge about Personality and Driving-related Behavior (2020.lrec-1)
Copied to clipboard
| Challenge: | Using crowdsourcing, we acquire human-specific knowledge about personality and driving. |
| Approach: | They propose a psychological approach to collect human-specific social knowledge from a text corpus using NLP techniques. |
| Outcome: | The proposed approach collects human-specific social knowledge from a text corpus, and then implements it into a system. |
Video-Helpful Multimodal Machine Translation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing multimodal machine translation datasets contain images and video captions or instructional video subtitles, which rarely contain linguistic ambiguity. |
| Approach: | They propose an MMT dataset that contains ambiguous subtitles and a video-helpful evaluation set. |
| Outcome: | The proposed model performs significantly better than existing models on ambiguous subtitles dataset . it is based on a training set and video-helpful evaluation set . |
Contextualized and Generalized Sentence Representations by Contrastive Self-Supervised Learning: A Case Study on Discourse Relation Analysis (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to learn contextualized and generalized sentence representations are limited by the size of manually annotated data. |
| Approach: | They propose a method to learn contextualized and generalized sentence representations using contrastive self-supervised learning. |
| Outcome: | The proposed method outperforms baseline methods based on BERT, XLNet, and RoBERTa in English and Japanese and outperformed strong baseline methods. |
Towards Speech Dialogue Translation Mediating Speakers of Different Languages (2023.findings-acl)
Copied to clipboard
| Challenge: | a new task is proposed to mediate speakers of different languages using speech dialogue translation . we consider context as an important aspect that needs to be addressed in this task . speech translation (ST) has also recently shown success in monologue translation - but no study has focused on ST of dialogues . |
| Approach: | They propose a task to mediate speakers of different languages using speech dialogue translation . they construct a speechBSD dataset and conduct baseline experiments . |
| Outcome: | The proposed task mediates speakers of different languages using speech dialogue translation dataset . it shows that bilingual context performs better in our settings . |
BERT-based Cohesion Analysis of Japanese Texts (2020.coling-main)
Copied to clipboard
| Challenge: | Recent advances in neural networks have significantly improved natural language processing tasks . they include self training-based language models such as BERT . |
| Approach: | They tackle a systematic analysis of cohesion in Japanese texts using BERT models . they find that coreference resolution is different in nature from other tasks . |
| Outcome: | The proposed analysis outperforms existing studies on cohesion in Japanese texts. |
A Method for Building a Commonsense Inference Dataset based on Basic Events (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to acquire commonsense are limited by the general-purpose language models. |
| Approach: | They propose a method for building a commonsense inference dataset using crowdsourcing and automatic extraction from a corpus. |
| Outcome: | The proposed method can solve 104k commonsense inference problems in a Japanese corpus with high accuracy, but low bias. |
Leveraging High-Resource English Corpora for Cross-lingual Domain Adaptation in Low-Resource Japanese Medicine via Continued Pre-training (2025.findings-emnlp)
Copied to clipboard
Kazuma Kobayashi, Zhen Wan, Fei Cheng, Yuma Tsuta, Xin Zhao, Junfeng Jiang, Jiahao Huang, Zhiyi Huang, Yusuke Oda, Rio Yokota, Yuki Arase, Daisuke Kawahara, Akiko Aizawa, Sadao Kurohashi
| Challenge: | low-resource language corpora in professional domains like medicine hinder cross-lingual domain adaptation of pre-trained large language models. |
| Approach: | They examine how linguistic features affect performance on a Japanese–English medical knowledge benchmark. |
| Outcome: | The proposed model can leverage English-language resources in medical domains while ensuring sufficient coverage of language-specific expressions in a target language. |
Exploring the Impact of Layer Normalization for Zero-shot Neural Machine Translation (2023.acl-short)
Copied to clipboard
| Challenge: | Recent studies have shown that layer normalization (LayerNorm) overfits training data and therefore has low generalizability for ZST. |
| Approach: | They propose to use the Transformer architecture to set the default layer normalization setting for zero-shot translation (ZST) they also propose to set LayerNorm after residual connections to outperform PreNorm by 12.3 BLEU points. |
| Outcome: | The proposed model outperforms the current model by 12.3 BLEU points on 54 directions on OPUS, IWSLT, and Europarl datasets. |
BERTSeg: BERT Based Unsupervised Subword Segmentation for Neural Machine Translation (2022.aacl-short)
Copied to clipboard
| Challenge: | Existing subword segmenters are frequency-based without semantics information or neural-based but trained on parallel corpora. |
| Approach: | They propose an unsupervised neural subword segmenter for neural machine translation that utilizes contextualized semantic embeddings of words from characterBERT and maximizes the generation probability of subword segments. |
| Outcome: | The proposed method improves translation performance on ALT, IWSLT15 Vi->En, WMT16 Ro->En and WMT15 Fi->En datasets. |
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation (2022.naacl-srw)
Copied to clipboard
| Challenge: | Hence, speaker modeling is important for the task of emotion recognition in conversation (ERC). |
| Approach: | They propose a graph-based ERC model which considers conversational context and speaker personality. |
| Outcome: | The proposed model outperforms baseline and other graph-based methods on a benchmark dataset. |