82 citations · 432 across the 55 of their papers we have counts for
56 papers
AdaST: Dynamically Adapting Encoder States in the Decoder for End-to-End Speech-to-Text Translation
Wuwei Huang, Dexin Wang, Deyi Xiong
In end-to-end speech translation, acoustic representations learned by the encoder are usually fixed and static, from the perspective of the decoder, which is not desirable for deal…
Joint Training And Decoding for Multilingual End-to-End Simultaneous Speech Translation
Wuwei Huang, Renren Jin, Wen Zhang +3
Recent studies on end-to-end speech translation(ST) have facilitated the exploration of multilingual end-to-end ST and end-to-end simultaneous ST. In this paper, we investigate end…
Evaluating Discourse Cohesion in Pre-trained Language Models
Jie He, Wanqiu Long, Deyi Xiong
Large pre-trained neural models have achieved remarkable success in natural language process (NLP), inspiring a growing body of research analyzing their ability from different aspe…
Tgea: An error-annotated dataset and benchmark tasks for text generation from pretrained language models
Jie He, Bo Peng, Yi Liao +2
In order to deeply understand the capability of pretrained language models in text generation and conduct a diagnostic evaluation, we propose TGEA, an error-annotated dataset with…
The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation
Jie He, Tao Wang, Deyi Xiong +1
Does neural machine translation yield translations that are congenial with common sense? In this paper, we present a test suite to evaluate the commonsense reasoning capability of…
Large Language Model Alignment: A Survey
Tianhao Shen, Renren Jin, Yufei Huang +6
Recent years have witnessed remarkable progress made in large language models (LLMs). Such advancements, while garnering significant attention, have concurrently elicited various c…
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
Mehrad Moradshahi, Tianhao Shen, Kalika Bali +16
Task-oriented dialogue research has mainly focused on a few popular languages like English and Chinese, due to the high dataset creation cost for a new language. To reduce the cost…
CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Models
Yufei Huang, Deyi Xiong
Holistically measuring societal biases of large language models is crucial for detecting and reducing ethical risks in highly capable AI models. In this work, we present a Chinese…
M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models
Chuang Liu, Renren Jin, Yuqi Ren +10
Large language models have recently made tremendous progress in a variety of aspects, e.g., cross-task generalization, instruction following. Comprehensively evaluating the capabil…
Inverse Reinforcement Learning for Text Summarization
Yu Fu, Deyi Xiong, Yue Dong
We introduce inverse reinforcement learning (IRL) as an effective paradigm for training abstractive summarization models, imitating human summarization behaviors. Our IRL model est…
Informative Language Representation Learning for Massively Multilingual Neural Machine Translation
Renren Jin, Deyi Xiong
In a multilingual neural machine translation model that fully shares parameters across all languages, an artificial language token is usually used to guide translation into the des…
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran +74
Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using…
Efficient Cluster-Based k-Nearest-Neighbor Machine Translation
Dexin Wang, Kai Fan, Boxing Chen +1
k-Nearest-Neighbor Machine Translation (kNN-MT) has been recently proposed as a non-parametric solution for domain adaptation in neural machine translation (NMT). It aims to allevi…
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension
Linjuan Wu, Shaojuan Wu, Xiaowang Zhang +4
Multilingual pre-trained models are able to zero-shot transfer knowledge from rich-resource to low-resource languages in machine reading comprehension (MRC). However, inherent ling…
Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network
Yuqi Ren, Deyi Xiong
Cognitive processing signals can be used to improve natural language processing (NLP) tasks. However, it is not clear how these signals correlate with linguistic information. Bridg…
Secoco: Self-Correcting Encoding for Neural Machine Translation
Tao Wang, Chengqi Zhao, Mingxuan Wang +3
This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with input noise for robust neural machine translation by introducing self-correcting pred…
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals
Yuqi Ren, Deyi Xiong
Most previous studies integrate cognitive language processing signals (e.g., eye-tracking or EEG data) into neural models of natural language processing (NLP) just by directly conc…
Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation
Tao Wang, Chengqi Zhao, Mingxuan Wang +2
Automatic translation of dialogue texts is a much needed demand in many real life scenarios. However, the currently existing neural machine translation delivers unsatisfying result…
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…
Integrating Pre-trained Model into Rule-based Dialogue Management
Jun Quan, Meng Yang, Qiang Gan +9
Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems for their interpretablility. However, it is hard for developers to m…
Efficient Object-Level Visual Context Modeling for Multimodal Machine Translation: Masking Irrelevant Objects Helps Grounding
Dexin Wang, Deyi Xiong
Visual context provides grounding information for multimodal machine translation (MMT). However, previous MMT models and probing studies on visual features suggest that visual info…
Modeling Homophone Noise for Robust Neural Machine Translation
Wenjie Qin, Xiang Li, Yuhui Sun +3
In this paper, we propose a robust neural machine translation (NMT) framework. The framework consists of a homophone noise detector and a syllable-aware NMT model to homophone erro…
RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling
Jun Quan, Shian Zhang, Qian Cao +2
In order to alleviate the shortage of multi-domain data and to capture discourse phenomena for task-oriented dialogue modeling, we propose RiSAWOZ, a large-scale multi-domain Chine…
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer
Yufang Huang, Wentao Zhu, Deyi Xiong +3
Unsupervised text style transfer is full of challenges due to the lack of parallel data and difficulties in content preservation. In this paper, we propose a novel neural approach…