In relation extraction for knowledge-based question answering, searching from one entity to
another entity via a single relation is called" one hop". In related work, an exhaustive search …

Attention-based models are successful when trained on large amounts of data. In this paper,
we demonstrate that even in the low-resource scenario, attention can be learned effectively …

Constituency parsing is a fundamental and important task for natural language
understanding, where a good representation of contextual information can help this task. N …

Image-text matching is an important multi-modal task with massive applications. It tries to
match the image and the text with similar semantic information. Existing approaches do not …

This paper provides an analytical assessment of student short answer responses with a view
to potential benefits in pedagogical contexts. We first propose and formalize two novel …

Constituency parsing is a fundamental yet unsolved natural language processing task. In
this paper, we explore the potential of recent large language models (LLMs) that have …

Sentence extractive summarization shortens a document by selecting sentences for a
summary while preserving its important contents. However, constructing a coherent and …

Sentence compression is the task of compressing a long sentence into a short one by
deleting redundant words. In sequence-to-sequence (Seq2Seq) based models, the decoder …

In text classification, the traditional attention mechanisms usually focus too much on frequent
words, and need extensive labeled data in order to learn. This article proposes a …

A sentence compression method using LSTM can generate fluent compressed sentences.
However, the performance of this method is significantly degraded when compressing …