Search Results for author: Will Monroe

Found 14 papers, 7 papers with code

Generating Bilingual Pragmatic Color References

1 code implementation NAACL 2018 Will Monroe, Jennifer Hu, Andrew Jong, Christopher Potts

Contextual influences on language often exhibit substantial cross-lingual regularities; for example, we are more verbose in situations that require finer distinctions.

Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding

1 code implementation TACL 2017 Will Monroe, Robert X. D. Hawkins, Noah D. Goodman, Christopher Potts

We present a model of pragmatic referring expression interpretation in a grounded communication task (identifying colors from descriptions) that draws upon predictions from two recurrent neural network classifiers, a speaker and a listener, unified by a recursive pragmatic reasoning framework.

Referring Expression

Data Distillation for Controlling Specificity in Dialogue Generation

no code implementations22 Feb 2017 Jiwei Li, Will Monroe, Dan Jurafsky

We show that from such a set of subsystems, one can use reinforcement learning to build a system that tailors its output to different input contexts at test time.

Dialogue Generation reinforcement-learning

Learning to Decode for Future Success

no code implementations23 Jan 2017 Jiwei Li, Will Monroe, Dan Jurafsky

We introduce a simple, general strategy to manipulate the behavior of a neural decoder that enables it to generate outputs that have specific properties of interest (e. g., sequences of a pre-specified length).

Abstractive Text Summarization Decision Making +2

Adversarial Learning for Neural Dialogue Generation

7 code implementations EMNLP 2017 Jiwei Li, Will Monroe, Tianlin Shi, Sébastien Jean, Alan Ritter, Dan Jurafsky

In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances.

Dialogue Evaluation Dialogue Generation

Understanding Neural Networks through Representation Erasure

no code implementations24 Dec 2016 Jiwei Li, Will Monroe, Dan Jurafsky

While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability.

Sentiment Analysis

A Simple, Fast Diverse Decoding Algorithm for Neural Generation

1 code implementation25 Nov 2016 Jiwei Li, Will Monroe, Dan Jurafsky

We further propose a variation that is capable of automatically adjusting its diversity decoding rates for different inputs using reinforcement learning (RL).

Abstractive Text Summarization Machine Translation +3

Deep Reinforcement Learning for Dialogue Generation

8 code implementations EMNLP 2016 Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes.

Dialogue Generation Policy Gradient Methods +1

Learning in the Rational Speech Acts Model

no code implementations23 Oct 2015 Will Monroe, Christopher Potts

The Rational Speech Acts (RSA) model treats language use as a recursive process in which probabilistic speaker and listener agents reason about each other's intentions to enrich the literal semantics of their language along broadly Gricean lines.

Text Generation

Text to 3D Scene Generation with Rich Lexical Grounding

no code implementations IJCNLP 2015 Angel Chang, Will Monroe, Manolis Savva, Christopher Potts, Christopher D. Manning

The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics.

Scene Generation

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