In recent years, conversational agents (CAs) have become ubiquitous and are a presence in
our daily routines. It seems that the technology has finally ripened to advance the use of CAs …

This paper presents the results of the premier shared task organized alongside the
Conference on Machine Translation (WMT) 2019. Participants were asked to build machine …

Specifically focusing on the landscape of abstractive text summarization, as opposed to
extractive techniques, this survey presents a comprehensive overview, delving into state-of …

We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in
several aspects. The language is human-written and less noisy. The dialogues in the …

The success of large pretrained language models (LMs) such as BERT and RoBERTa has
sparked interest in probing their representations, in order to unveil what types of knowledge …

In this paper, we survey the methods and concepts developed for the evaluation of dialogue
systems. Evaluation, in and of itself, is a crucial part during the development process. Often …

Sequence-to-sequence neural network models for generation of conversational responses
tend to generate safe, commonplace responses (eg," I don't know") regardless of the input …

We present persona-based models for handling the issue of speaker consistency in neural
response generation. A speaker model encodes personas in distributed embeddings that …

This paper presents the results of the WMT16 shared tasks, which included five machine
translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three …

Conversational modeling is an important task in natural language understanding and
machine intelligence. Although previous approaches exist, they are often restricted to …