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Found 19 papers, 3 papers with code
Hitachi at SemEval-2022 Task 2: On the Effectiveness of Span-based Classification Approaches for Multilingual Idiomaticity Detection
In this paper, we describe our system for SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding.
Hitachi at SemEval-2022 Task 10: Comparing Graph- and Seq2Seq-based Models Highlights Difficulty in Structured Sentiment Analysis
In this task, we have to parse opinions considering both structure- and context-dependent subjective aspects, which is different from typical dependency parsing.
Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic
We rethink this and adopt a well-grounded set of deduction rules based on formal logic theory, which can derive any other deduction rules when combined in a multistep way.
How does the task complexity of masked pretraining objectives affect downstream performance?
Masked language modeling (MLM) is a widely used self-supervised pretraining objective, where a model needs to predict an original token that is replaced with a mask given contexts.
Rethinking Fano's Inequality in Ensemble Learning
We propose a fundamental theory on ensemble learning that answers the central question: what factors make an ensemble system good or bad?
Team Hitachi @ AutoMin 2021: Reference-free Automatic Minuting Pipeline with Argument Structure Construction over Topic-based Summarization
This paper introduces the proposed automatic minuting system of the Hitachi team for the First Shared Task on Automatic Minuting (AutoMin-2021).
Project-then-Transfer: Effective Two-stage Cross-lingual Transfer for Semantic Dependency Parsing
This paper describes the first report on cross-lingual transfer for semantic dependency parsing.
Hitachi at SemEval-2020 Task 7: Stacking at Scale with Heterogeneous Language Models for Humor Recognition
Our experimental results show that SaS outperforms a naive average ensemble, leveraging weaker PLMs as well as high-performing PLMs.
Hitachi at SemEval-2020 Task 3: Exploring the Representation Spaces of Transformers for Human Sense Word Similarity
Due to the unsupervised nature of the task, we concentrated on inquiring about the similarity measures induced by different layers of different pre-trained Transformer-based language models, which can be good approximations of the human sense of word similarity.
Hitachi at SemEval-2020 Task 8: Simple but Effective Modality Ensemble for Meme Emotion Recognition
Users of social networking services often share their emotions via multi-modal content, usually images paired with text embedded in them.
Hitachi at SemEval-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models
This paper shows our system for SemEval-2020 task 10, Emphasis Selection for Written Text in Visual Media.
Hitachi at SemEval-2020 Task 11: An Empirical Study of Pre-Trained Transformer Family for Propaganda Detection
In this paper, we show our system for SemEval-2020 task 11, where we tackle propaganda span identification (SI) and technique classification (TC).
Hitachi at MRP 2020: Text-to-Graph-Notation Transducer
This paper presents our proposed parser for the shared task on Meaning Representation Parsing (MRP 2020) at CoNLL, where participant systems were required to parse five types of graphs in different languages.
Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization
Our proposed model incorporates (i) task-specific parameterization (TSP) that effectively encodes a sequence of propositions and (ii) a proposition-level biaffine attention (PLBA) that can predict a non-tree argument consisting of edges.
Corpus for Modeling User Interactions in Online Persuasive Discussions
To analyze persuasive strategies, it is important to understand how individuals construct posts and comments based on the semantics of the argumentative components.
Revealing and Predicting Online Persuasion Strategy with Elementary Units
In online arguments, identifying how users construct their arguments to persuade others is important in order to understand a persuasive strategy directly.
Hitachi at MRP 2019: Unified Encoder-to-Biaffine Network for Cross-Framework Meaning Representation Parsing
This paper describes the proposed system of the Hitachi team for the Cross-Framework Meaning Representation Parsing (MRP 2019) shared task.
Annotating and Analyzing Semantic Role of Elementary Units and Relations in Online Persuasive Arguments
For analyzing online persuasions, one of the important goals is to semantically understand how people construct comments to persuade others.
End-to-End Argument Mining for Discussion Threads Based on Parallel Constrained Pointer Architecture
Argument Mining (AM) is a relatively recent discipline, which concentrates on extracting claims or premises from discourses, and inferring their structures.
