38 citations · 99 across the 23 of their papers we have counts for
23 papers
Image Matters: A New Dataset and Empirical Study for Multimodal Hyperbole Detection
Huixuan Zhang, Xiaojun Wan
Hyperbole, or exaggeration, is a common linguistic phenomenon. The detection of hyperbole is an important part of understanding human expression. There have been several studies on…
Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework
Mingqi Gao, Xiaojun Wan, Jia Su +2
Factuality is important to dialogue summarization. Factual error correction (FEC) of model-generated summaries is one way to improve factuality. Current FEC evaluation that relies…
SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning
Yunxiang Zhang, Xiaojun Wan
Recently, commonsense reasoning in text generation has attracted much attention. Generative commonsense reasoning is the task that requires machines, given a group of keywords, to…
Is Summary Useful or Not? An Extrinsic Human Evaluation of Text Summaries on Downstream Tasks
Xiao Pu, Mingqi Gao, Xiaojun Wan
Research on automated text summarization relies heavily on human and automatic evaluation. While recent work on human evaluation mainly adopted intrinsic evaluation methods, judgin…
Teaching the Pre-trained Model to Generate Simple Texts for Text Simplification
Renliang Sun, Wei Xu, Xiaojun Wan
Randomly masking text spans in ordinary texts in the pre-training stage hardly allows models to acquire the ability to generate simple texts. It can hurt the performance of pre-tra…
Human-like Summarization Evaluation with ChatGPT
Mingqi Gao, Jie Ruan, Renliang Sun +3
Evaluating text summarization is a challenging problem, and existing evaluation metrics are far from satisfactory. In this study, we explored ChatGPT's ability to perform human-lik…
Models See Hallucinations: Evaluating the Factuality in Video Captioning
Hui Liu, Xiaojun Wan
Video captioning aims to describe events in a video with natural language. In recent years, many works have focused on improving captioning models' performance. However, like other…
Exploiting Summarization Data to Help Text Simplification
Renliang Sun, Zhixian Yang, Xiaojun Wan
One of the major problems with text simplification is the lack of high-quality data. The sources of simplification datasets are limited to Wikipedia and Newsela, restricting furthe…
How to Describe Images in a More Funny Way? Towards a Modular Approach to Cross-Modal Sarcasm Generation
Jie Ruan, Yue Wu, Xiaojun Wan +1
Sarcasm generation has been investigated in previous studies by considering it as a text-to-text generation problem, i.e., generating a sarcastic sentence for an input sentence. In…
Social Biases in Automatic Evaluation Metrics for NLG
Mingqi Gao, Xiaojun Wan
Many studies have revealed that word embeddings, language models, and models for specific downstream tasks in NLP are prone to social biases, especially gender bias. Recently these…
An Empirical Study of Automatic Post-Editing
Xu Zhang, Xiaojun Wan
Automatic post-editing (APE) aims to reduce manual post-editing efforts by automatically correcting errors in machine-translated output. Due to the limited amount of human-annotate…
Nearest Neighbor Knowledge Distillation for Neural Machine Translation
Zhixian Yang, Renliang Sun, Xiaojun Wan
k-nearest-neighbor machine translation (NN-MT), proposed by Khandelwal et al. (2021), has achieved many state-of-the-art results in machine translation tasks. Although effective, N…
SimpleBERT: A Pre-trained Model That Learns to Generate Simple Words
Renliang Sun, Xiaojun Wan
Pre-trained models are widely used in the tasks of natural language processing nowadays. However, in the specific field of text simplification, the research on improving pre-traine…
Dependency-based Mixture Language Models
Zhixian Yang, Xiaojun Wan
Various models have been proposed to incorporate knowledge of syntactic structures into neural language models. However, previous works have relied heavily on elaborate components…
A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis
Xiang Chen, Xiaojun Wan
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task which aims to extract the aspects from sentences and identify their corresponding sentiments. Aspec…
Visual Information Guided Zero-Shot Paraphrase Generation
Zhe Lin, Xiaojun Wan
Zero-shot paraphrase generation has drawn much attention as the large-scale high-quality paraphrase corpus is limited. Back-translation, also known as the pivot-based method, is ty…
Neural Content Extraction for Poster Generation of Scientific Papers
Sheng Xu, Xiaojun Wan
The problem of poster generation for scientific papers is under-investigated. Posters often present the most important information of papers, and the task can be considered as a sp…
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
Yuan Yao, Qingxiu Dong, Jian Guan +32
Realizing general-purpose language intelligence has been a longstanding goal for natural language processing, where standard evaluation benchmarks play a fundamental and guiding ro…
A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correction
Zhaohong Wan, Xiaojun Wan
Grammatical Error Correction (GEC) is a task of detecting and correcting grammatical errors in sentences. Recently, neural machine translation systems have become popular approache…
Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling
Zhixian Yang, Pengxuan Xu, Xiaojun Wan
Neural text generation models are likely to suffer from the low-diversity problem. Various decoding strategies and training-based methods have been proposed to promote diversity on…
Learning a Product Relevance Model from Click-Through Data in E-Commerce
Shaowei Yao, Jiwei Tan, Xi Chen +4
The search engine plays a fundamental role in online e-commerce systems, to help users find the products they want from the massive product collections. Relevance is an essential r…
ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation
Qingxiu Dong, Xiaojun Wan, Yue Cao
We propose ParaSCI, the first large-scale paraphrase dataset in the scientific field, including 33,981 paraphrase pairs from ACL (ParaSCI-ACL) and 316,063 pairs from arXiv (ParaSCI…
TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis
Zilong Wang, Zhaohong Wan, Xiaojun Wan
Multimodal sentiment analysis is an important research area that predicts speaker's sentiment tendency through features extracted from textual, visual and acoustic modalities. The…