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Found 15 papers, 7 papers with code
Text to 3D Scene Generation with Rich Lexical Grounding
The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics.
Learning in the Rational Speech Acts Model
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.
Deep Reinforcement Learning for Dialogue Generation
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.
Learning to Generate Compositional Color Descriptions
The production of color language is essential for grounded language generation.
A Simple, Fast Diverse Decoding Algorithm for Neural Generation
We further propose a variation that is capable of automatically adjusting its diversity decoding rates for different inputs using reinforcement learning (RL).
Understanding Neural Networks through Representation Erasure
While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability.
Adversarial Learning for Neural Dialogue Generation
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.
Ranked #1 on
Dialogue Generation
on Amazon-5
Learning to Decode for Future Success
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).
Data Distillation for Controlling Specificity in Dialogue Generation
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.
Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding
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.
IcoRating: A Deep-Learning System for Scam ICO Identification
For the best setting, the proposed system is able to identify scam ICO projects with 0. 83 precision.
Generating Bilingual Pragmatic Color References
Contextual influences on language often exhibit substantial cross-lingual regularities; for example, we are more verbose in situations that require finer distinctions.
Simultaneous Translation and Paraphrase for Language Education
We present the task of Simultaneous Translation and Paraphrasing for Language Education (STAPLE).
Large Language Model Augmented Exercise Retrieval for Personalized Language Learning
We study the problem of zero-shot exercise retrieval in the context of online language learning, to give learners the ability to explicitly request personalized exercises via natural language.
