A Large-Scale Corpus for Conversation Disentanglement (Kummerfeld et al., 2019)

This post is about my own paper to appear at ACL later this month. What is interesting about this paper will depend on your research interests, so that’s how I’ve broken down this blog post. A few key points first: Data and code are available on Github. The paper is also available. The general-purpose span labeling and linking annotation tool we used is also appearing at ACL. Check out DSTC 8 Track 2, which is based on this work.

Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time (Huang et al., 2018)

For a more flexible dialogue system, use the crowd to propose and vote on responses, then introduce agents and a model for voting, gradually learning to replace the crowd.

Frames: a corpus for adding memory to goal-oriented dialogue systems (El Asri et al., 2017)

A new dialogue dataset that has annotations of multiple plans (frames) and dialogue acts that indicate modifications to them.

Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings (He et al., 2017)

During task-oriented dialogue generation, to take into consideration a table of information about entities, represent it as a graph, run message passing to get vector representations of each entity, and use attention.

Joint Modeling of Content and Discourse Relations in Dialogues (Qin et al., 2017)

Identifying the key phrases in a dialogue at the same time as identifying the type of relations between pairs of utterances leads to substantial improvements on both tasks.