Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

Language Models (LMs) often cannot be deployed because of their potential to harm users
in hard-to-predict ways. Prior work identifies harmful behaviors before deployment by using …

Neural text generation is a key tool in natural language applications, but it is well known
there are major problems at its core. In particular, standard likelihood training and decoding …

We present our view of what is necessary to build an engaging open-domain conversational
agent: covering the qualities of such an agent, the pieces of the puzzle that have been built …

We study generating abstractive summaries that are faithful and factually consistent with the
given articles. A novel contrastive learning formulation is presented, which leverages both …

Pre-trained language models (LMs) store knowledge in their parameters and can generate
informative responses when used in conversational systems. However, LMs suffer from the …

Generative dialogue models currently suffer from a number of problems which standard
maximum likelihood training does not address. They tend to produce generations that (i) rely …

We present CAiRE, an end-to-end generative empathetic chatbot designed to recognize
user emotions and respond in an empathetic manner. Our system adapts the Generative Pre …

Standard language model training employs gold human documents or human-human
interaction data, and treats all training data as positive examples. Growing evidence shows …

With the rapid progress of deep neural models and the explosion of available data
resources, dialogue systems that supports extensive topics and chit-chat conversations are …