83 citations · 517 across the 59 of their papers we have counts for
8 papers · 1 filter
Towards Better Document-level Relation Extraction via Iterative Inference
Liang Zhang, Jinsong Su, Yidong Chen +4
Document-level relation extraction (RE) aims to extract the relations between entities from the input document that usually containing many difficultly-predicted entity pairs whose…
Getting the Most out of Simile Recognition
Xiaoyue Wang, Linfeng Song, Xin Liu +2
Simile recognition involves two subtasks: simile sentence classification that discriminates whether a sentence contains simile, and simile component extraction that locates the cor…
WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation
Binbin Xie, Xiangpeng Wei, Baosong Yang +5
Keyphrase generation aims to automatically generate short phrases summarizing an input document. The recently emerged ONE2SET paradigm (Ye et al., 2021) generates keyphrases as a s…
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis
Shuai Fan, Chen Lin, Haonan Li +6
Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-c…
Towards Robust k-Nearest-Neighbor Machine Translation
Hui Jiang, Ziyao Lu, Fandong Meng +4
k-Nearest-Neighbor Machine Translation (kNN-MT) becomes an important research direction of NMT in recent years. Its main idea is to retrieve useful key-value pairs from an addition…
A Variational Hierarchical Model for Neural Cross-Lingual Summarization
Yunlong Liang, Fandong Meng, Chulun Zhou +4
The goal of the cross-lingual summarization (CLS) is to convert a document in one language (e.g., English) to a summary in another one (e.g., Chinese). Essentially, the CLS task is…
Type-Driven Multi-Turn Corrections for Grammatical Error Correction
Shaopeng Lai, Qingyu Zhou, Jiali Zeng +4
Grammatical Error Correction (GEC) aims to automatically detect and correct grammatical errors. In this aspect, dominant models are trained by one-iteration learning while performi…
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation
Chulun Zhou, Fandong Meng, Jie Zhou +3
Most dominant neural machine translation (NMT) models are restricted to make predictions only according to the local context of preceding words in a left-to-right manner. Although…