Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems.Avenues like the E2E NLG Challenge have encouraged the development of neural approaches, particularly sequence-tosequence (Seq2Seq) models for this problem.The semantic representations used, however, are often underspecified, which places a higher burden on the generation model for sentence planning, and also limits the extent to which generated responses can be controlled in a live system.In this paper, we (1) propose using tree-structured semantic representations, like those used in traditional rule-based NLG systems, for better discourse-level structuring and sentence-level planning; (2) introduce a challenging dataset using this representation for the weather domain; (3) introduce a constrained decoding approach for Seq2Seq models that leverages this representation to improve semantic correctness; and (4) demonstrate promising results on our dataset and the E2E dataset.

Dongyeop Kang, Anusha Balakrishnan, Pararth Shah, Paul Crook, Y-Lan Boureau, Jason Weston. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems.Sequence-to-sequence models on flat meaning representations (MR) have been dominant in this task, for example in the E2E NLG Challenge.Previous work has shown that a tree-structured MR can improve the model for better discourse-level structuring and sentence-level planning.In this work, we propose a tree-to-sequence model that uses a tree-LSTM encoder to leverage the tree structures in the input MR, and further enhance the decoding by a structure-enhanced attention mechanism.In addition, we explore combining these enhancements with constrained decoding to improve semantic correctness.Our method not only shows significant improvements over standard seq2seq baselines, but also is more data-efficient and generalizes better to hard scenarios.

We consider negotiation settings in which two agents use natural language to bargain on goods.Agents need to decide on both high-level strategy (e.g., proposing $50) and the execution of that strategy (e.g., generating "The bike is brand new.Selling for just $50!").Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy, and reinforcement learning tends to lead to degenerate solutions.In this paper, we propose a modular approach based on coarse dialogue acts (e.g., propose(price=50)) that decouples strategy and generation.We show that we can flexibly set the strategy using supervised learning, reinforcement learning, or domain-specific knowledge without degeneracy, while our retrieval-based generation can maintain context-awareness and produce diverse utterances.We test our approach on the recently proposed DEALORNODEAL game, and we also collect a richer dataset based on real items on Craigslist.Human evaluation shows that our systems achieve higher task success rate and more human-like negotiation behavior than previous approaches.

We study a symmetric collaborative dialogue setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal.The open-ended dialogue state in this setting poses new challenges for existing dialogue systems.We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements.To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses.Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.