83 citations · 517 across the 59 of their papers we have counts for
11 papers · 1 filter
A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognition
Changxing Wu, Liuwen Cao, Yubin Ge +3
Implicit discourse relation recognition (IDRR) is a challenging but crucial task in discourse analysis. Most existing methods train multiple models to predict multi-level labels in…
KGR^4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation
Xin Liu, Dayiheng Liu, Baosong Yang +6
Generative commonsense reasoning requires machines to generate sentences describing an everyday scenario given several concepts, which has attracted much attention recently. Howeve…
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings
Shaopeng Lai, Ante Wang, Fandong Meng +6
Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models,…
Towards Making the Most of Dialogue Characteristics for Neural Chat Translation
Yunlong Liang, Chulun Zhou, Fandong Meng +4
Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages. Despite the promising performance of sentence-level and context-aware n…
Controllable Dialogue Generation with Disentangled Multi-grained Style Specification and Attribute Consistency Reward
Zhe Hu, Zhiwei Cao, Hou Pong Chan +4
Controllable text generation is an appealing but challenging task, which allows users to specify particular attributes of the generated outputs. In this paper, we propose a control…
Towards User-Driven Neural Machine Translation
Huan Lin, Liang Yao, Baosong Yang +5
A good translation should not only translate the original content semantically, but also incarnate personal traits of the original text. For a real-world neural machine translation…
Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation
Xin Liu, Baosong Yang, Dayiheng Liu +5
A well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary. This potentially weakens the effect when applying pretra…
Exploring Dynamic Selection of Branch Expansion Orders for Code Generation
Hui Jiang, Chulun Zhou, Fandong Meng +5
Due to the great potential in facilitating software development, code generation has attracted increasing attention recently. Generally, dominant models are Seq2Tree models, which…
Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning
Binbin Xie, Jinsong Su, Yubin Ge +4
Code generation aims to automatically generate a piece of code given an input natural language utterance. Currently, among dominant models, it is treated as a sequence-to-tree task…
Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention Learning
Jinsong Su, Jialong Tang, Hui Jiang +6
In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction. Ho…
Structural Information Preserving for Graph-to-Text Generation
Linfeng Song, Ante Wang, Jinsong Su +4
The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or…