most citedDataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering

68 citations · 139 across the 10 of their papers we have counts for

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cs.CL20231 cited

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…

cs.CL2023

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…

cs.CL20232 cited

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…

cs.CL20169 cited

Discovering Conversational Dependencies between Messages in Dialogs

Wenchao Du, Pascal Poupart, Wei Xu

We investigate the task of inferring conversational dependencies between messages in one-on-one online chat, which has become one of the most popular forms of customer service. We…

cs.CL201630 cited

Neural Machine Translation with Pivot Languages

Yong Cheng, Yang Liu, Qian Yang +2

While recent neural machine translation approaches have delivered state-of-the-art performance for resource-rich language pairs, they suffer from the data scarcity problem for reso…

cs.CL201668 cited

Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering

Peng Li, Wei Li, Zhengyan He +4

While question answering (QA) with neural network, i.e. neural QA, has achieved promising results in recent years, lacking of large scale real-word QA dataset is still a challenge…