Papers by Daichi Mochihashi
Learning Adverbs with Spectral Mixture Kernels (2024.findings-acl)
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| Challenge: | In order for robots to collaborate with humans, it is important to share and understand their experiences through language. |
| Approach: | They propose a hierarchical Dirichlet Process-Spectral Mixture Latent Dirichlets Allocation model which learns the relationship between human motions and adverbs by capturing frequency kernels that represent motion characteristics and shared topics of a given aadverts. |
| Outcome: | The proposed model outperforms representative neural network models in terms of perplexity score and predicts more appropriate adverbs. |
Scale-Invariant Infinite Hierarchical Topic Model (2023.findings-acl)
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| Challenge: | Existing hierarchical topic models yield fragmented topics with overlapping themes whose expected probability becomes exponentially smaller along the depth of the tree. |
| Approach: | They propose a hierarchical infinite hierarchic topic model that adapts to topic creation to make expected topic probability decay considerably slower than existing models. |
| Outcome: | The proposed model has better topic uniqueness and hierarchical diversity than existing approaches. |
Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices (2025.coling-main)
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| Challenge: | Existing methods to analyze word sense proportions are insufficient for understanding semantic shifts . et al., 2018: semantic shift and its effects. |
| Approach: | They propose a framework for how semantic shifts occur over multiple time periods by using word embeddings. |
| Outcome: | The proposed framework can analyze semantic shifts over multiple time periods using word embeddings. |
Holographic CCG Parsing (2023.acl-long)
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| Challenge: | Existing methods for supertagging and parsing use black-box neural architectures to implicitly model phrase structure dependencies. |
| Approach: | They propose a method for formulating CCG as a recursive composition in a continuous vector space by using holographic embeddings as holography operator. |
| Outcome: | The proposed method can achieve comparable performance to state-of-the-art parsing with Transformers. |
How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text (2020.coling-main)
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| Challenge: | LSTMs are widely used to capture informative long-term syntactic dependencies, but how they are reflected in their internal vectors for natural text has not been adequately investigated. |
| Approach: | They analyze how syntactic dependencies are reflected in LSTM's internal gates by learning a language model where syntaktic structures are implicitly given. |
| Outcome: | The proposed model can predict whether a word is inside a phrase structure or not from a small number of components of the context-update vector. |
Cross-lingual and Word-Independent Methods for Quantifying Degree of Grammaticalization (2026.eacl-long)
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Ryo Nagata, Daichi Mochihashi, Misato Ido, Yusuke Kubota, Naoki Otani, Yoshifumi Kawasaki, Hiroya Takamura
| Challenge: | Existing methods for quantifying the degree of grammaticalization are language- and word-dependent . existing methods are language dependent and lack training data . |
| Approach: | They propose to use Positive-Unlabeled learning or Cross-Validation-like learning to quantify degree of grammaticalization. |
| Outcome: | The proposed method achieves high correlations to human judgments in English deverbal prepositions and Japanese nouns being grammaticalized. |
Infinite SCAN: An Infinite Model of Diachronic Semantic Change (2022.emnlp-main)
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| Challenge: | Existing methods for capturing semantic changes using word embeddings cannot account for existence of each sense and its relative importance. |
| Approach: | They propose a Bayesian model that can estimate the number of senses of words and their changes through time using a dynamic topic model and a logistic stick-breaking process. |
| Outcome: | The proposed model outperforms the baseline model and investigates the semantic changes of several well-known target words using the CCOHA corpus. |