67 citations · 339 across the 48 of their papers we have counts for
32 papers · 1 filter
Med-CoReasoner: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning
Fan Gao, Sherry T. Tong, Jiwoong Sohn +11
While reasoning-enhanced large language models perform strongly on English medical tasks, a persistent multilingual gap remains, with substantially weaker reasoning in local langua…
Probing Physical Reasoning with Counter-Commonsense Context
Kazushi Kondo, Saku Sugawara, Akiko Aizawa
In this study, we create a CConS (Counter-commonsense Contextual Size comparison) dataset to investigate how physical commonsense affects the contextualized size comparison task; t…
SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation
Junfeng Jiang, Chengzhang Dong, Sadao Kurohashi +1
Dialogue segmentation is a crucial task for dialogue systems allowing a better understanding of conversational texts. Despite recent progress in unsupervised dialogue segmentation…
Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara +1
To explain the predicted answers and evaluate the reasoning abilities of models, several studies have utilized underlying reasoning (UR) tasks in multi-hop question answering (QA)…
Which Shortcut Solution Do Question Answering Models Prefer to Learn?
Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa
Question answering (QA) models for reading comprehension tend to learn shortcut solutions rather than the solutions intended by QA datasets. QA models that have learned shortcut so…
Penalizing Confident Predictions on Largely Perturbed Inputs Does Not Improve Out-of-Distribution Generalization in Question Answering
Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa
Question answering (QA) models are shown to be insensitive to large perturbations to inputs; that is, they make correct and confident predictions even when given largely perturbed…
Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles
Timo Spinde, Jan-David Krieger, Terry Ruas +4
Media has a substantial impact on the public perception of events. A one-sided or polarizing perspective on any topic is usually described as media bias. One of the ways how bias i…
Debiasing Masks: A New Framework for Shortcut Mitigation in NLU
Johannes Mario Meissner, Saku Sugawara, Akiko Aizawa
Debiasing language models from unwanted behaviors in Natural Language Understanding tasks is a topic with rapidly increasing interest in the NLP community. Spurious statistical cor…
Look to the Right: Mitigating Relative Position Bias in Extractive Question Answering
Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa
Extractive question answering (QA) models tend to exploit spurious correlations to make predictions when a training set has unintended biases. This tendency results in models not b…
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?
Xanh Ho, Saku Sugawara, Akiko Aizawa
Several multi-hop reading comprehension datasets have been proposed to resolve the issue of reasoning shortcuts by which questions can be answered without performing multi-hop reas…
Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts
Timo Spinde, Manuel Plank, Jan-David Krieger +3
Media coverage has a substantial effect on the public perception of events. Nevertheless, media outlets are often biased. One way to bias news articles is by altering the word choi…
A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension
Xanh Ho, Johannes Mario Meissner, Saku Sugawara +1
The issue of shortcut learning is widely known in NLP and has been an important research focus in recent years. Unintended correlations in the data enable models to easily solve ta…
Gender Biases and Where to Find Them: Exploring Gender Bias in Pre-Trained Transformer-based Language Models Using Movement Pruning
Przemyslaw Joniak, Akiko Aizawa
Language model debiasing has emerged as an important field of study in the NLP community. Numerous debiasing techniques were proposed, but bias ablation remains an unaddressed issu…
Do BERTs Learn to Use Browser User Interface? Exploring Multi-Step Tasks with Unified Vision-and-Language BERTs
Taichi Iki, Akiko Aizawa
Pre-trained Transformers are good foundations for unified multi-task models owing to their task-agnostic representation. Pre-trained Transformers are often combined with text-to-te…
Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical Overlap
Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa
Question answering (QA) models for reading comprehension have been demonstrated to exploit unintended dataset biases such as question-context lexical overlap. This hinders QA model…
Embracing Ambiguity: Shifting the Training Target of NLI Models
Johannes Mario Meissner, Napat Thumwanit, Saku Sugawara +1
Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels. While many research works do not pay much attention to this fact, several recent efforts ha…
Maintaining Common Ground in Dynamic Environments
Takuma Udagawa, Akiko Aizawa
Common grounding is the process of creating and maintaining mutual understandings, which is a critical aspect of sophisticated human communication. While various task settings have…
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language Models
Taichi Iki, Akiko Aizawa
A method for creating a vision-and-language (V&L) model is to extend a language model through structural modifications and V&L pre-training. Such an extension aims to make a V&L mo…
Multi-sense embeddings through a word sense disambiguation process
Terry Ruas, William Grosky, Akiko Aizawa
Natural Language Understanding has seen an increasing number of publications in the last few years, especially after robust word embeddings models became prominent, when they prove…
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara +1
A multi-hop question answering (QA) dataset aims to test reasoning and inference skills by requiring a model to read multiple paragraphs to answer a given question. However, curren…
Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems
Vitou Phy, Yang Zhao, Akiko Aizawa
Many automatic evaluation metrics have been proposed to score the overall quality of a response in open-domain dialogue. Generally, the overall quality is comprised of various aspe…
A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions
Takuma Udagawa, Takato Yamazaki, Akiko Aizawa
Recent models achieve promising results in visually grounded dialogues. However, existing datasets often contain undesirable biases and lack sophisticated linguistic analyses, whic…
A System for Worldwide COVID-19 Information Aggregation
Akiko Aizawa, Frederic Bergeron, Junjie Chen +26
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhil…
Extraction and Evaluation of Formulaic Expressions Used in Scholarly Papers
Kenichi Iwatsuki, Florian Boudin, Akiko Aizawa
Formulaic expressions, such as 'in this paper we propose', are helpful for authors of scholarly papers because they convey communicative functions; in the above, it is showing the…