Kei Uchiumi


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Optimizing Word Segmentation for Downstream Task
Tatsuya Hiraoka | Sho Takase | Kei Uchiumi | Atsushi Keyaki | Naoaki Okazaki
Findings of the Association for Computational Linguistics: EMNLP 2020

In traditional NLP, we tokenize a given sentence as a preprocessing, and thus the tokenization is unrelated to a target downstream task. To address this issue, we propose a novel method to explore a tokenization which is appropriate for the downstream task. Our proposed method, optimizing tokenization (OpTok), is trained to assign a high probability to such appropriate tokenization based on the downstream task loss. OpTok can be used for any downstream task which uses a vector representation of a sentence such as text classification. Experimental results demonstrate that OpTok improves the performance of sentiment analysis and textual entailment. In addition, we introduce OpTok into BERT, the state-of-the-art contextualized embeddings and report a positive effect.

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How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text
Chihiro Shibata | Kei Uchiumi | Daichi Mochihashi
Proceedings of the 28th International Conference on Computational Linguistics

Long Short-Term Memory recurrent neural network (LSTM) is widely used and known to capture informative long-term syntactic dependencies. However, how such information are reflected in its internal vectors for natural text has not yet been sufficiently investigated. We analyze them by learning a language model where syntactic structures are implicitly given. We empirically show that the context update vectors, i.e. outputs of internal gates, are approximately quantized to binary or ternary values to help the language model to count the depth of nesting accurately, as Suzgun et al. (2019) recently show for synthetic Dyck languages. For some dimensions in the context vector, we show that their activations are highly correlated with the depth of phrase structures, such as VP and NP. Moreover, with an L1 regularization, we also found that it can accurately predict whether a word is inside a phrase structure or not from a small number of components of the context vector. Even for the case of learning from raw text, context vectors are shown to still correlate well with the phrase structures. Finally, we show that natural clusters of the functional words and the part of speeches that trigger phrases are represented in a small but principal subspace of the context-update vector of LSTM.


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Inducing Word and Part-of-Speech with Pitman-Yor Hidden Semi-Markov Models
Kei Uchiumi | Hiroshi Tsukahara | Daichi Mochihashi
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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System Utterance Generation by Label Propagation over Association Graph of Words and Utterance Patterns for Open-Domain Dialogue Systems
Hiroshi Tsukahara | Kei Uchiumi
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation


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Japanese Abbreviation Expansion with Query and Clickthrough Logs
Kei Uchiumi | Mamoru Komachi | Keigo Machinaga | Toshiyuki Maezawa | Toshinori Satou | Yoshinori Kobayashi
Proceedings of 5th International Joint Conference on Natural Language Processing


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Learning Semantic Categories from Clickthrough Logs
Mamoru Komachi | Shimpei Makimoto | Kei Uchiumi | Manabu Sassano
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers