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Found 11 papers, 7 papers with code
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.
Unsupervised Domain Adaptation on Question-Answering System with Conversation Data
Therefore, unlike previous studies, we propose a domain-adaptation framework of MRC under the assumption that the only available data in the target domain are human conversations between a user asking questions and an expert answering the questions.
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.
Text Retrieval with Multi-Stage Re-Ranking Models
We ranked the documents by BM25 and language models, and then re-ranks by a model ensemble or a larger language model for documents with high similarity to the query.
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.
LARCH: Large Language Model-based Automatic Readme Creation with Heuristics
Writing a readme is a crucial aspect of software development as it plays a vital role in managing and reusing program code.
How do different tokenizers perform on downstream tasks in scriptio continua languages?: A case study in Japanese
This paper investigates the effect of tokenizers on the downstream performance of pretrained language models (PLMs) in scriptio continua languages where no explicit spaces exist between words, using Japanese as a case study.
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.
Controlling keywords and their positions in text generation
In this paper, we tackle a novel task of controlling not only keywords but also the position of each keyword in the text generation.
Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News
Based on the multilingual, multi-task nature of the task and the low-resource setting, we investigated different cross-lingual and multi-task strategies for training the pretrained language models.
An Interactive Greedy Approach to Group Sparsity in High Dimensions
Sparsity learning with known grouping structure has received considerable attention due to wide modern applications in high-dimensional data analysis.
Human Activity Recognition
Vocal Bursts Intensity Prediction
