Practitioners increasingly use machine learning (ML) models, yet models have become
more complex and harder to understand. To understand complex models, researchers have …

Utterance rewriting aims to recover coreferences and omitted information from the latest turn
of a multi-turn dialogue. Recently, methods that tag rather than linearly generate sequences …

Though chatbots based on large neural models can often produce fluent responses in open
domain conversations, one salient error type is contradiction or inconsistency with the …

Understanding context is key to understanding human language, an ability which Large
Language Models (LLMs) have been increasingly seen to demonstrate to an impressive …

It has recently become feasible to run personal digital assistants on phones and other
personal devices. In this paper we describe a design for a natural language understanding …

Communicating efficiently in natural language requires that we often leave information
implicit, especially in spontaneous speech. This frequently results in phenomena of …

Conversational understanding is an integral part of modern intelligent devices. In a large
fraction of the global traffic from customers using smart digital assistants, frictions in …

The state of bridging resolution research is rather unsatisfactory: not only are state-of-the-art
resolvers evaluated in unrealistic settings, but the neural models underlying these resolvers …

Rewriting incomplete and ambiguous utterances can improve dialogue models'
understanding of the context and help them generate better results. However, the existing …

Utterance rewriting aims to identify and supply the omitted information in human
conversation, which further enables the downstream task to understand conversations more …