This paper focuses on paraphrase generation, which is a widely studied natural language
generation task in NLP. With the development of neural models, paraphrase generation …

Despite the advancement of question answering (QA) systems and rapid improvements on
held-out test sets, their generalizability is a topic of concern. We explore the robustness of …

Paraphrasing natural language sentences is a multifaceted process: it might involve
replacing individual words or short phrases, local rearrangement of content, or high-level …

Different texts shall by nature correspond to different number of keyphrases. This
desideratum is largely missing from existing neural keyphrase generation models. In this …

Metaphor generation is a difficult task, and has seen tremendous improvement with the
advent of deep pretrained models. We focus here on the specific task of metaphoric …

Paraphrases refer to texts that convey the same meaning with different expression forms.
Traditional seq2seq-based models on paraphrase generation mainly focus on the fidelity …

Large scale Pre-trained Language Models have proven to be very powerful approach in
various Natural language tasks. OpenAI's GPT-2\cite {radford2019language} is notable for …

Building conversational task-oriented bots requires large and diverse sets of annotated user
utterances to learn mappings between natural language utterances and user intents. Given …

A long-standing issue with paraphrase generation is how to obtain reliable supervision
signals. In this paper, we propose an unsupervised paradigm for paraphrase generation …

Users of machine translation systems may desire to obtain multiple candidates translated in
different ways. In this work, we attempt to obtain diverse translations by using sentence …