Tatsuki Kuribayashi


2023

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Empirical Investigation of Neural Symbolic Reasoning Strategies
Yoichi Aoki | Keito Kudo | Tatsuki Kuribayashi | Ana Brassard | Masashi Yoshikawa | Keisuke Sakaguchi | Kentaro Inui
Findings of the Association for Computational Linguistics: EACL 2023

Neural reasoning accuracy improves when generating intermediate reasoning steps. However, the source of this improvement is yet unclear. Here, we investigate and factorize the benefit of generating intermediate steps for symbolic reasoning. Specifically, we decompose the reasoning strategy w.r.t. 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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Transformer Language Models Handle Word Frequency in Prediction Head
Goro Kobayashi | Tatsuki Kuribayashi | Sho Yokoi | Kentaro Inui
Findings of the Association for Computational Linguistics: ACL 2023

Prediction head is a crucial component of Transformer language models. Despite its direct impact on prediction, this component has often been overlooked in analyzing Transformers.In this study, we investigate the inner workings of the prediction head, specifically focusing on bias parameters. Our experiments with BERT and GPT-2 models reveal that the biases in their word prediction heads play a significant role in the models’ ability to reflect word frequency in a corpus, aligning with the logit adjustment method commonly used in long-tailed learning. We also quantify the effect of controlling the biases in practical auto-regressive text generation scenarios;under a particular setting, more diverse text can be generated without compromising text quality.

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Second Language Acquisition of Neural Language Models
Miyu Oba | Tatsuki Kuribayashi | Hiroki Ouchi | Taro Watanabe
Findings of the Association for Computational Linguistics: ACL 2023

With the success of neural language models (LMs), their language acquisition has gained much attention. This work sheds light on the second language (L2) acquisition of LMs, while previous work has typically explored their first language (L1) acquisition. Specifically, we trained bilingual LMs with a scenario similar to human L2 acquisition and analyzed their cross-lingual transfer from linguistic perspectives. Our exploratory experiments demonstrated that the L1 pretraining accelerated their linguistic generalization in L2, and language transfer configurations (e.g., the L1 choice, and presence of parallel texts) substantially affected their generalizations. These clarify their (non-)human-like L2 acquisition in particular aspects.

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Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism
Mengyu Ye | Tatsuki Kuribayashi | Jun Suzuki | Goro Kobayashi | Hiroaki Funayama
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Large language models (LLMs) take advantage of step-by-step reasoning instructions, e.g., chain-of-thought (CoT) prompting. Building on this, their ability to perform CoT-style reasoning robustly is of interest from a probing perspective. In this study, we inspect the step-by-step reasoning ability of LLMs with a focus on negation, which is a core linguistic phenomenon that is difficult to process. In particular, we introduce several controlled settings (e.g., reasoning in case of fictional entities) to evaluate the logical reasoning abilities of the models. We observed that dozens of modern LLMs were not robust against lexical negation (e.g., plausibleimplausible) when performing CoT-style reasoning, and the results highlight unique limitations in each LLM family.

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Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?
Keito Kudo | Yoichi Aoki | Tatsuki Kuribayashi | Ana Brassard | Masashi Yoshikawa | Keisuke Sakaguchi | Kentaro Inui
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Compositionality is a pivotal property of symbolic reasoning. However, how well recent neural models capture compositionality remains underexplored in the symbolic reasoning tasks. This study empirically addresses this question by systematically examining recently published pre-trained seq2seq models with a carefully controlled dataset of multi-hop arithmetic symbolic reasoning. We introduce a skill tree on compositionality in arithmetic symbolic reasoning that defines the hierarchical levels of complexity along with three compositionality dimensions: systematicity, productivity, and substitutivity. Our experiments revealed that among the three types of composition, the models struggled most with systematicity, performing poorly even with relatively simple compositions. That difficulty was not resolved even after training the models with intermediate reasoning steps.

2022

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Topicalization in Language Models: A Case Study on Japanese
Riki Fujihara | Tatsuki Kuribayashi | Kaori Abe | Ryoko Tokuhisa | Kentaro Inui
Proceedings of the 29th International Conference on Computational Linguistics

Humans use different wordings depending on the context to facilitate efficient communication. For example, instead of completely new information, information related to the preceding context is typically placed at the sentence-initial position. In this study, we analyze whether neural language models (LMs) can capture such discourse-level preferences in text generation. Specifically, we focus on a particular aspect of discourse, namely the topic-comment structure. To analyze the linguistic knowledge of LMs separately, we chose the Japanese language, a topic-prominent language, for designing probing tasks, and we created human topicalization judgment data by crowdsourcing. Our experimental results suggest that LMs have different generalizations from humans; LMs exhibited less context-dependent behaviors toward topicalization judgment. These results highlight the need for the additional inductive biases to guide LMs to achieve successful discourse-level generalization.

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Context Limitations Make Neural Language Models More Human-Like
Tatsuki Kuribayashi | Yohei Oseki | Ana Brassard | Kentaro Inui
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Language models (LMs) have been used in cognitive modeling as well as engineering studies—they compute information-theoretic complexity metrics that simulate humans’ cognitive load during reading. This study highlights a limitation of modern neural LMs as the model of choice for this purpose: there is a discrepancy between their context access capacities and that of humans. Our results showed that constraining the LMs’ context access improved their simulation of human reading behavior. We also showed that LM-human gaps in context access were associated with specific syntactic constructions; incorporating syntactic biases into LMs’ context access might enhance their cognitive plausibility.

2021

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Lower Perplexity is Not Always Human-Like
Tatsuki Kuribayashi | Yohei Oseki | Takumi Ito | Ryo Yoshida | Masayuki Asahara | Kentaro Inui
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In computational psycholinguistics, various language models have been evaluated against human reading behavior (e.g., eye movement) to build human-like computational models. However, most previous efforts have focused almost exclusively on English, despite the recent trend towards linguistic universal within the general community. In order to fill the gap, this paper investigates whether the established results in computational psycholinguistics can be generalized across languages. Specifically, we re-examine an established generalization —the lower perplexity a language model has, the more human-like the language model is— in Japanese with typologically different structures from English. Our experiments demonstrate that this established generalization exhibits a surprising lack of universality; namely, lower perplexity is not always human-like. Moreover, this discrepancy between English and Japanese is further explored from the perspective of (non-)uniform information density. Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.

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Incorporating Residual and Normalization Layers into Analysis of Masked Language Models
Goro Kobayashi | Tatsuki Kuribayashi | Sho Yokoi | Kentaro Inui
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Transformer architecture has become ubiquitous in the natural language processing field. To interpret the Transformer-based models, their attention patterns have been extensively analyzed. However, the Transformer architecture is not only composed of the multi-head attention; other components can also contribute to Transformers’ progressive performance. In this study, we extended the scope of the analysis of Transformers from solely the attention patterns to the whole attention block, i.e., multi-head attention, residual connection, and layer normalization. Our analysis of Transformer-based masked language models shows that the token-to-token interaction performed via attention has less impact on the intermediate representations than previously assumed. These results provide new intuitive explanations of existing reports; for example, discarding the learned attention patterns tends not to adversely affect the performance. The codes of our experiments are publicly available.

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Instance-Based Neural Dependency Parsing
Hiroki Ouchi | Jun Suzuki | Sosuke Kobayashi | Sho Yokoi | Tatsuki Kuribayashi | Masashi Yoshikawa | Kentaro Inui
Transactions of the Association for Computational Linguistics, Volume 9

Interpretable rationales for model predictions are crucial in practical applications. We develop neural models that possess an interpretable inference process for dependency parsing. Our models adopt instance-based inference, where dependency edges are extracted and labeled by comparing them to edges in a training set. The training edges are explicitly used for the predictions; thus, it is easy to grasp the contribution of each edge to the predictions. Our experiments show that our instance-based models achieve competitive accuracy with standard neural models and have the reasonable plausibility of instance-based explanations.

2020

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Attention is Not Only a Weight: Analyzing Transformers with Vector Norms
Goro Kobayashi | Tatsuki Kuribayashi | Sho Yokoi | Kentaro Inui
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Attention is a key component of Transformers, which have recently achieved considerable success in natural language processing. Hence, attention is being extensively studied to investigate various linguistic capabilities of Transformers, focusing on analyzing the parallels between attention weights and specific linguistic phenomena. This paper shows that attention weights alone are only one of the two factors that determine the output of attention and proposes a norm-based analysis that incorporates the second factor, the norm of the transformed input vectors. The findings of our norm-based analyses of BERT and a Transformer-based neural machine translation system include the following: (i) contrary to previous studies, BERT pays poor attention to special tokens, and (ii) reasonable word alignment can be extracted from attention mechanisms of Transformer. These findings provide insights into the inner workings of Transformers.

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Langsmith: An Interactive Academic Text Revision System
Takumi Ito | Tatsuki Kuribayashi | Masatoshi Hidaka | Jun Suzuki | Kentaro Inui
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Despite the current diversity and inclusion initiatives in the academic community, researchers with a non-native command of English still face significant obstacles when writing papers in English. This paper presents the Langsmith editor, which assists inexperienced, non-native researchers to write English papers, especially in the natural language processing (NLP) field. Our system can suggest fluent, academic-style sentences to writers based on their rough, incomplete phrases or sentences. The system also encourages interaction between human writers and the computerized revision system. The experimental results demonstrated that Langsmith helps non-native English-speaker students write papers in English. The system is available at https://emnlp-demo.editor.langsmith.co.jp/.

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Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese
Tatsuki Kuribayashi | Takumi Ito | Jun Suzuki | Kentaro Inui
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We examine a methodology using neural language models (LMs) for analyzing the word order of language. This LM-based method has the potential to overcome the difficulties existing methods face, such as the propagation of preprocessor errors in count-based methods. In this study, we explore whether the LM-based method is valid for analyzing the word order. As a case study, this study focuses on Japanese due to its complex and flexible word order. To validate the LM-based method, we test (i) parallels between LMs and human word order preference, and (ii) consistency of the results obtained using the LM-based method with previous linguistic studies. Through our experiments, we tentatively conclude that LMs display sufficient word order knowledge for usage as an analysis tool. Finally, using the LM-based method, we demonstrate the relationship between the canonical word order and topicalization, which had yet to be analyzed by large-scale experiments.

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Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
Hiroki Ouchi | Jun Suzuki | Sosuke Kobayashi | Sho Yokoi | Tatsuki Kuribayashi | Ryuto Konno | Kentaro Inui
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Interpretable rationales for model predictions play a critical role in practical applications. In this study, we develop models possessing interpretable inference process for structured prediction. Specifically, we present a method of instance-based learning that learns similarities between spans. At inference time, each span is assigned a class label based on its similar spans in the training set, where it is easy to understand how much each training instance contributes to the predictions. Through empirical analysis on named entity recognition, we demonstrate that our method enables to build models that have high interpretability without sacrificing performance.

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Modeling Event Salience in Narratives via Barthes’ Cardinal Functions
Takaki Otake | Sho Yokoi | Naoya Inoue | Ryo Takahashi | Tatsuki Kuribayashi | Kentaro Inui
Proceedings of the 28th International Conference on Computational Linguistics

Events in a narrative differ in salience: some are more important to the story than others. Estimating event salience is useful for tasks such as story generation, and as a tool for text analysis in narratology and folkloristics. To compute event salience without any annotations, we adopt Barthes’ definition of event salience and propose several unsupervised methods that require only a pre-trained language model. Evaluating the proposed methods on folktales with event salience annotation, we show that the proposed methods outperform baseline methods and find fine-tuning a language model on narrative texts is a key factor in improving the proposed methods.

2019

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An Empirical Study of Span Representations in Argumentation Structure Parsing
Tatsuki Kuribayashi | Hiroki Ouchi | Naoya Inoue | Paul Reisert | Toshinori Miyoshi | Jun Suzuki | Kentaro Inui
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

For several natural language processing (NLP) tasks, span representation design is attracting considerable attention as a promising new technique; a common basis for an effective design has been established. With such basis, exploring task-dependent extensions for argumentation structure parsing (ASP) becomes an interesting research direction. This study investigates (i) span representation originally developed for other NLP tasks and (ii) a simple task-dependent extension for ASP. Our extensive experiments and analysis show that these representations yield high performance for ASP and provide some challenging types of instances to be parsed.

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Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance
Takumi Ito | Tatsuki Kuribayashi | Hayato Kobayashi | Ana Brassard | Masato Hagiwara | Jun Suzuki | Kentaro Inui
Proceedings of the 12th International Conference on Natural Language Generation

The writing process consists of several stages such as drafting, revising, editing, and proofreading. Studies on writing assistance, such as grammatical error correction (GEC), have mainly focused on sentence editing and proofreading, where surface-level issues such as typographical errors, spelling errors, or grammatical errors should be corrected. We broaden this focus to include the earlier revising stage, where sentences require adjustment to the information included or major rewriting and propose Sentence-level Revision (SentRev) as a new writing assistance task. Well-performing systems in this task can help inexperienced authors by producing fluent, complete sentences given their rough, incomplete drafts. We build a new freely available crowdsourced evaluation dataset consisting of incomplete sentences authored by non-native writers paired with their final versions extracted from published academic papers for developing and evaluating SentRev models. We also establish baseline performance on SentRev using our newly built evaluation dataset.

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TEASPN: Framework and Protocol for Integrated Writing Assistance Environments
Masato Hagiwara | Takumi Ito | Tatsuki Kuribayashi | Jun Suzuki | Kentaro Inui
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

Language technologies play a key role in assisting people with their writing. Although there has been steady progress in e.g., grammatical error correction (GEC), human writers are yet to benefit from this progress due to the high development cost of integrating with writing software. We propose TEASPN, a protocol and an open-source framework for achieving integrated writing assistance environments. The protocol standardizes the way writing software communicates with servers that implement such technologies, allowing developers and researchers to integrate the latest developments in natural language processing (NLP) with low cost. As a result, users can enjoy the integrated experience in their favorite writing software. The results from experiments with human participants show that users use a wide range of technologies and rate their writing experience favorably, allowing them to write more fluent text.

2018

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Feasible Annotation Scheme for Capturing Policy Argument Reasoning using Argument Templates
Paul Reisert | Naoya Inoue | Tatsuki Kuribayashi | Kentaro Inui
Proceedings of the 5th Workshop on Argument Mining

Most of the existing works on argument mining cast the problem of argumentative structure identification as classification tasks (e.g. attack-support relations, stance, explicit premise/claim). This paper goes a step further by addressing the task of automatically identifying reasoning patterns of arguments using predefined templates, which is called argument template (AT) instantiation. The contributions of this work are three-fold. First, we develop a simple, yet expressive set of easily annotatable ATs that can represent a majority of writer’s reasoning for texts with diverse policy topics while maintaining the computational feasibility of the task. Second, we create a small, but highly reliable annotated corpus of instantiated ATs on top of reliably annotated support and attack relations and conduct an annotation study. Third, we formulate the task of AT instantiation as structured prediction constrained by a feasible set of templates. Our evaluation demonstrates that we can annotate ATs with a reasonably high inter-annotator agreement, and the use of template-constrained inference is useful for instantiating ATs with only partial reasoning comprehension clues.