Legal judgment prediction (LJP) applies Natural Language Processing (NLP) techniques to
predict judgment results based on fact descriptions automatically. The present work …

Code summarization aims to generate brief natural language descriptions for source codes.
The state-of-the-art approaches follow a transformer-based encoder-decoder architecture …

We study the problem of generating arithmetic math word problems (MWPs) given a math
equation that specifies the mathematical computation and a context that specifies the …

Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-
diversity problem when it comes to open-domain dialogue generation. As bland and generic …

The neural attention model has achieved great success in data-to-text generation tasks.
Though usually excelling at producing fluent text, it suffers from the problem of information …

Large Language Models pre-trained with self-supervised learning have demonstrated
impressive zero-shot generalization capabilities on a wide spectrum of tasks. In this work …

In open domain table-to-text generation, we notice the unfaithful generation usually contains
hallucinated entities which can not be aligned to any input table record. We thus try to …

Being able to perform in-depth chat with humans in a closed domain is a precondition before
an open-domain chatbot can be ever claimed. In this work, we take a close look at the movie …

State-of-the-art neural models typically encode document-query pairs using cross-attention
for re-ranking. To this end, models generally utilize an encoder-only (like BERT) paradigm or …

We study the task of long-form opinion text generation, which faces at least two distinct
challenges. First, existing neural generation models fall short of coherence, thus requiring …