83 citations · 517 across the 59 of their papers we have counts for
59 papers
Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering
Yifan Dong, Suhang Wu, Fandong Meng +4
Multi-modal keyphrase generation aims to produce a set of keyphrases that represent the core points of the input text-image pair. In this regard, dominant methods mainly focus on m…
DocTER: Evaluating Document-based Knowledge Editing
Suhang Wu, Ante Wang, Minlong Peng +4
Knowledge editing aims to correct outdated or inaccurate knowledge in neural networks. In this paper, we explore knowledge editing using easily accessible documents instead of manu…
On the Cultural Gap in Text-to-Image Generation
Bingshuai Liu, Longyue Wang, Chenyang Lyu +4
One challenge in text-to-image (T2I) generation is the inadvertent reflection of culture gaps present in the training data, which signifies the disparity in generated image quality…
A Simple yet Effective Self-Debiasing Framework for Transformer Models
Xiaoyue Wang, Lijie Wang, Xin Liu +3
Current Transformer-based natural language understanding (NLU) models heavily rely on dataset biases, while failing to handle real-world out-of-distribution (OOD) instances. Many m…
Exploring Better Text Image Translation with Multimodal Codebook
Zhibin Lan, Jiawei Yu, Xiang Li +5
Text image translation (TIT) aims to translate the source texts embedded in the image to target translations, which has a wide range of applications and thus has important research…
Revisiting Non-Autoregressive Translation at Scale
Zhihao Wang, Longyue Wang, Jinsong Su +2
In real-world systems, scaling has been critical for improving the translation quality in autoregressive translation (AT), which however has not been well studied for non-autoregre…
A Sequence-to-Sequence&Set Model for Text-to-Table Generation
Tong Li, Zhihao Wang, Liangying Shao +3
Recently, the text-to-table generation task has attracted increasing attention due to its wide applications. In this aspect, the dominant model formalizes this task as a sequence-t…
Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation
Zhiwei Cao, Baosong Yang, Huan Lin +6
-Nearest neighbor machine translation (NN-MT) has attracted increasing attention due to its ability to non-parametrically adapt to new translation domains. By using an upstre…
RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training
Chulun Zhou, Yunlong Liang, Fandong Meng +3
Multilingual vision-language (V&L) pre-training has achieved remarkable progress in learning universal representations across different modalities and languages. In spite of recent…
From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey
Binbin Xie, Jia Song, Liangying Shao +6
Keyphrase prediction aims to generate phrases (keyphrases) that highly summarizes a given document. Recently, researchers have conducted in-depth studies on this task from various…
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation
Liyan Kang, Luyang Huang, Ningxin Peng +6
We present a large-scale video subtitle translation dataset, BigVideo, to facilitate the study of multi-modality machine translation. Compared with the widely used How2 and VaTeX d…
Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production
Ante Wang, Linfeng Song, Qi Liu +5
Knowledge-aided dialogue response generation aims at augmenting chatbots with relevant external knowledge in the hope of generating more informative responses. The majority of prev…
A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation
Chulun Zhou, Yunlong Liang, Fandong Meng +5
Neural chat translation (NCT) aims to translate a cross-lingual chat between speakers of different languages. Existing context-aware NMT models cannot achieve satisfactory performa…
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…
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,…