Masashi Yoshikawa


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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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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Tracing and Manipulating intermediate values in Neural Math Problem Solvers
Yuta Matsumoto | Benjamin Heinzerling | Masashi Yoshikawa | Kentaro Inui
Proceedings of the 1st Workshop on Mathematical Natural Language Processing (MathNLP)

How language models process complex input that requires multiple steps of inference is not well understood. Previous research has shown that information about intermediate values of these inputs can be extracted from the activations of the models, but it is unclear where that information is encoded and whether that information is indeed used during inference. We introduce a method for analyzing how a Transformer model processes these inputs by focusing on simple arithmetic problems and their intermediate values. To trace where information about intermediate values is encoded, we measure the correlation between intermediate values and the activations of the model using principal component analysis (PCA). Then, we perform a causal intervention by manipulating model weights. This intervention shows that the weights identified via tracing are not merely correlated with intermediate values, but causally related to model predictions. Our findings show that the model has a locality to certain intermediate values, and this is useful for enhancing the interpretability of the models.

2021

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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.

2019

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Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation
Masashi Yoshikawa | Hiroshi Noji | Koji Mineshima | Daisuke Bekki
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees. Our solution is conceptually simple, and not relying on a specific parser architecture, making it applicable to the current best-performing parsers. We conduct extensive parsing experiments with detailed discussion; on top of existing benchmark datasets on (1) biomedical texts and (2) question sentences, we create experimental datasets of (3) speech conversation and (4) math problems. When applied to the proposed method, an off-the-shelf CCG parser shows significant performance gains, improving from 90.7% to 96.6% on speech conversation, and from 88.5% to 96.8% on math problems.

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Multimodal Logical Inference System for Visual-Textual Entailment
Riko Suzuki | Hitomi Yanaka | Masashi Yoshikawa | Koji Mineshima | Daisuke Bekki
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

A large amount of research about multimodal inference across text and vision has been recently developed to obtain visually grounded word and sentence representations. In this paper, we use logic-based representations as unified meaning representations for texts and images and present an unsupervised multimodal logical inference system that can effectively prove entailment relations between them. We show that by combining semantic parsing and theorem proving, the system can handle semantically complex sentences for visual-textual inference.

2018

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Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning
Masashi Yoshikawa | Koji Mineshima | Hiroshi Noji | Daisuke Bekki
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

In formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain their logical formulas. Here, it is important that the parser processes the sentences consistently; failing to recognize the similar syntactic structure results in inconsistent predicate argument structures among them, in which case the succeeding theorem proving is doomed to failure. In this work, we present a simple method to extend an existing CCG parser to parse a set of sentences consistently, which is achieved with an inter-sentence modeling with Markov Random Fields (MRF). When combined with existing logic-based systems, our method always shows improvement in the RTE experiments on English and Japanese languages.

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Neural sentence generation from formal semantics
Kana Manome | Masashi Yoshikawa | Hitomi Yanaka | Pascual Martínez-Gómez | Koji Mineshima | Daisuke Bekki
Proceedings of the 11th International Conference on Natural Language Generation

Sequence-to-sequence models have shown strong performance in a wide range of NLP tasks, yet their applications to sentence generation from logical representations are underdeveloped. In this paper, we present a sequence-to-sequence model for generating sentences from logical meaning representations based on event semantics. We use a semantic parsing system based on Combinatory Categorial Grammar (CCG) to obtain data annotated with logical formulas. We augment our sequence-to-sequence model with masking for predicates to constrain output sentences. We also propose a novel evaluation method for generation using Recognizing Textual Entailment (RTE). Combining parsing and generation, we test whether or not the output sentence entails the original text and vice versa. Experiments showed that our model outperformed a baseline with respect to both BLEU scores and accuracies in RTE.

2017

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A* CCG Parsing with a Supertag and Dependency Factored Model
Masashi Yoshikawa | Hiroshi Noji | Yuji Matsumoto
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We propose a new A* CCG parsing model in which the probability of a tree is decomposed into factors of CCG categories and its syntactic dependencies both defined on bi-directional LSTMs. Our factored model allows the precomputation of all probabilities and runs very efficiently, while modeling sentence structures explicitly via dependencies. Our model achieves the state-of-the-art results on English and Japanese CCG parsing.

2016

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Joint Transition-based Dependency Parsing and Disfluency Detection for Automatic Speech Recognition Texts
Masashi Yoshikawa | Hiroyuki Shindo | Yuji Matsumoto
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing