35 citations · 93 across the 11 of their papers we have counts for
11 papers
Multilingual Syntax-aware Language Modeling through Dependency Tree Conversion
Shunsuke Kando, Hiroshi Noji, Yusuke Miyao
Incorporating stronger syntactic biases into neural language models (LMs) is a long-standing goal, but research in this area often focuses on modeling English text, where constitue…
Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars
Ryo Yoshida, Hiroshi Noji, Yohei Oseki
In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like. However, the previous literature has been agnostic about a…
Effective Batching for Recurrent Neural Network Grammars
Hiroshi Noji, Yohei Oseki
As a language model that integrates traditional symbolic operations and flexible neural representations, recurrent neural network grammars (RNNGs) have attracted great attention fr…
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters
Hicham El Boukkouri, Olivier Ferret, Thomas Lavergne +3
Due to the compelling improvements brought by BERT, many recent representation models adopted the Transformer architecture as their main building block, consequently inheriting the…
An Analysis of the Utility of Explicit Negative Examples to Improve the Syntactic Abilities of Neural Language Models
Hiroshi Noji, Hiroya Takamura
We explore the utilities of explicit negative examples in training neural language models. Negative examples here are incorrect words in a sentence, such as "barks" in "*The dogs b…
Learning to Select, Track, and Generate for Data-to-Text
Hayate Iso, Yui Uehara, Tatsuya Ishigaki +6
We propose a data-to-text generation model with two modules, one for tracking and the other for text generation. Our tracking module selects and keeps track of salient information…
Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation
Masashi Yoshikawa, Hiroshi Noji, Koji Mineshima +1
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…
Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference
Masashi Yoshikawa, Koji Mineshima, Hiroshi Noji +1
In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data. However, there is…
Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning
Masashi Yoshikawa, Koji Mineshima, Hiroshi Noji +1
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 the…
A* CCG Parsing with a Supertag and Dependency Factored Model
Masashi Yoshikawa, Hiroshi Noji, Yuji Matsumoto
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…
Left-corner Methods for Syntactic Modeling with Universal Structural Constraints
Hiroshi Noji
The primary goal in this thesis is to identify better syntactic constraint or bias, that is language independent but also efficiently exploitable during sentence processing. We foc…