113 citations · 220 across the 29 of their papers we have counts for
23 papers · 1 filter
Thresh: A Unified, Customizable and Deployable Platform for Fine-Grained Text Evaluation
David Heineman, Yao Dou, Wei Xu
Fine-grained, span-level human evaluation has emerged as a reliable and robust method for evaluating text generation tasks such as summarization, simplification, machine translatio…
Improved Instruction Ordering in Recipe-Grounded Conversation
Duong Minh Le, Ruohao Guo, Wei Xu +1
In this paper, we study the task of instructional dialogue and focus on the cooking domain. Analyzing the generated output of the GPT-J model, we reveal that the primary challenge…
Revisiting non-English Text Simplification: A Unified Multilingual Benchmark
Michael J. Ryan, Tarek Naous, Wei Xu
Recent advancements in high-quality, large-scale English resources have pushed the frontier of English Automatic Text Simplification (ATS) research. However, less work has been don…
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…
Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding
Ruohao Guo, Wei Xu, Alan Ritter
Language style is often used by writers to convey their intentions, identities, and mastery of language. In this paper, we show that current large language models struggle to captu…
ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment
Tarek Naous, Michael J. Ryan, Anton Lavrouk +2
We present a comprehensive evaluation of large language models for multilingual readability assessment. Existing evaluation resources lack domain and language diversity, limiting t…
Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA
David Heineman, Yao Dou, Mounica Maddela +1
Large language models (e.g., GPT-4) are uniquely capable of producing highly rated text simplification, yet current human evaluation methods fail to provide a clear understanding o…
Multilingual Simplification of Medical Texts
Sebastian Joseph, Kathryn Kazanas, Keziah Reina +4
Automated text simplification aims to produce simple versions of complex texts. This task is especially useful in the medical domain, where the latest medical findings are typicall…
Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models
Junmo Kang, Wei Xu, Alan Ritter
Fine-tuning large models is highly effective, however, inference can be expensive and produces carbon emissions. Knowledge distillation has been shown to be a practical solution to…
LENS: A Learnable Evaluation Metric for Text Simplification
Mounica Maddela, Yao Dou, David Heineman +1
Training learnable metrics using modern language models has recently emerged as a promising method for the automatic evaluation of machine translation. However, existing human eval…
Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments
Ethan Mendes, Yang Chen, Wei Xu +1
We present a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that support them. Our approach extracts che…
Improving Large-scale Paraphrase Acquisition and Generation
Yao Dou, Chao Jiang, Wei Xu
This paper addresses the quality issues in existing Twitter-based paraphrase datasets, and discusses the necessity of using two separate definitions of paraphrase for identificatio…
Frustratingly Easy Label Projection for Cross-lingual Transfer
Yang Chen, Chao Jiang, Alan Ritter +1
Translating training data into many languages has emerged as a practical solution for improving cross-lingual transfer. For tasks that involve span-level annotations, such as infor…
arXivEdits: Understanding the Human Revision Process in Scientific Writing
Chao Jiang, Wei Xu, Samuel Stevens
Scientific publications are the primary means to communicate research discoveries, where the writing quality is of crucial importance. However, prior work studying the human editin…
Stanceosaurus: Classifying Stance Towards Multilingual Misinformation
Jonathan Zheng, Ashutosh Baheti, Tarek Naous +2
We present Stanceosaurus, a new corpus of 28,033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims. As far as we are aware, it is the lar…
BiSECT: Learning to Split and Rephrase Sentences with Bitexts
Joongwon Kim, Mounica Maddela, Reno Kriz +2
An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary.…
Pre-train or Annotate? Domain Adaptation with a Constrained Budget
Fan Bai, Alan Ritter, Wei Xu
Recent work has demonstrated that pre-training in-domain language models can boost performance when adapting to a new domain. However, the costs associated with pre-training raise…
Neural semi-Markov CRF for Monolingual Word Alignment
Wuwei Lan, Chao Jiang, Wei Xu
Monolingual word alignment is important for studying fine-grained editing operations (i.e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphra…
Extracting a Knowledge Base of COVID-19 Events from Social Media
Shi Zong, Ashutosh Baheti, Wei Xu +1
In this paper, we present a manually annotated corpus of 10,000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied acce…
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering
Wuwei Lan, Wei Xu
In this paper, we analyze several neural network designs (and their variations) for sentence pair modeling and compare their performance extensively across eight datasets, includin…
Character-based Neural Networks for Sentence Pair Modeling
Wuwei Lan, Wei Xu
Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference. Most state-of-the-art neural…
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols
Chaitanya Kulkarni, Wei Xu, Alan Ritter +1
We describe an effort to annotate a corpus of natural language instructions consisting of 622 wet lab protocols to facilitate automatic or semi-automatic conversion of protocols in…
A Continuously Growing Dataset of Sentential Paraphrases
Wuwei Lan, Siyu Qiu, Hua He +1
A major challenge in paraphrase research is the lack of parallel corpora. In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linki…