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Found 11 papers, 6 papers with code
The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding
Satisfying these constraints simultaneously is difficult for the two predominant paradigms in language generation: neural language modeling and rule-based generation.
The OSU/Facebook Realizer for SRST 2019: Seq2Seq Inflection and Serialized Tree2Tree Linearization
We describe our exploratory system for the shallow surface realization task, which combines morphological inflection using character sequence-to-sequence models with a baseline linearizer that implements a tree-to-tree model using sequence-to-sequence models on serialized trees.
A Tree-to-Sequence Model for Neural NLG in Task-Oriented Dialog
Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems.
Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue
These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone's preferences, react to their requests, and recommend more appropriate items.
Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue
Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems.
Generate, Filter, and Rank: Grammaticality Classification for Production-Ready NLG Systems
While acceptability includes grammatical correctness and semantic correctness, we focus only on grammaticality classification in this paper, and show that existing datasets for grammatical error correction don't correctly capture the distribution of errors that data-driven generators are likely to make.
Decoupling Strategy and Generation in Negotiation Dialogues
We consider negotiation settings in which two agents use natural language to bargain on goods.
Malaria Likelihood Prediction By Effectively Surveying Households Using Deep Reinforcement Learning
We build a deep reinforcement learning (RL) agent that can predict the likelihood of an individual testing positive for malaria by asking questions about their household.
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
