Month: October 2016

Please join us for the NLP Seminar this Monday, Oct 31 at 3:30pm in 202 South Hall.  All are welcome!

Speaker: Jiwei Li (Stanford)

Title: Teaching Machines to Converse

Abstract:

Recent neural networks models present both new opportunities and new challenges for developing conversational agents. In this talk, I will describe how we have advanced this line of research by addressing four different issues in neural dialogue generation: (1) overcoming the overwhelming prevalence of dull responses (e.g., “I don’t know”) generated from neural models; (2) enforcing speaker consistency; (3) applying reinforcement learning to foster sustained dialogue interactions. (4) How to teach a bot to interact with users and ask questions about things that he does not know.

(Slides)

Please join us for the NLP Seminar this Monday (Oct 17) at 3:30pm in 202 South Hall.

Speaker: Jacob Andreas (Berkeley)

Title: Reasoning about pragmatics with neural listeners and speakers

Abstract:

We present a model for contrastively describing scenes, in which context-specific behavior results from a combination of inference-driven pragmatics and learned semantics. Like previous learned approaches to language generation, our model uses a simple feature-driven architecture (here a pair of neural “listener” and “speaker” models) to ground language in the world. Like inference-driven approaches to pragmatics, our model actively reasons about listener behavior when selecting utterances. For training, our approach requires only ordinary captions, annotated without demonstration of the pragmatic behavior the model ultimately exhibits. In human evaluations on a referring expression game, our approach succeeds 81% of the time, compared to 69% using existing techniques.

Preparatory Readings:

• Golland, Liang, and Klein. “A game-theoretic approach to generating spatial descriptions.” EMNLP, 2010.
• Mao, Junhua, et al. “Generation and comprehension of unambiguous object descriptions.” https://arxiv.org/pdf/1511.02283v3.pdf