Large language models (LLMs) excel at processing and generating both text and code.However, LLMs have had limited applicability in grounded task-oriented dialogue as they are difficult to steer toward task objectives and fail to handle novel grounding.We present a modular and interpretable grounded dialogue system that addresses these shortcomings by composing LLMs with a symbolic planner and grounded code execution.Our system consists of a reader and planner: the reader leverages an LLM to convert partner utterances into executable code, calling functions that perform grounding.The translated code's output is stored to track dialogue state, while a symbolic planner determines the next appropriate response.We evaluate our system's performance on the demanding ONECOMMON dialogue task, involving collaborative reference resolution on abstract images of scattered dots.Our system substantially outperforms the previous state-of-the-art, including improving task success in human evaluations from 56% to 69% in the most challenging setting.

Training dialogue systems often entails dealing with noisy training examples and unexpected user inputs.Despite their prevalence, there currently lacks an accurate survey of dialogue noise, nor is there a clear sense of the impact of each noise type on task performance.This paper addresses this gap by first constructing a taxonomy of noise encountered by dialogue systems.In addition, we run a series of experiments to show how different models behave when subjected to varying levels of noise and types of noise.Our results reveal that models are quite robust to label errors commonly tackled by existing denoising algorithms, but that performance suffers from dialogue-specific noise.Driven by these observations, we design a data cleaning algorithm specialized for conversational settings and apply it as a proof-ofconcept for targeted dialogue denoising.Num.

Collecting data for training dialog systems can be extremely expensive due to the involvement of human participants and the need for extensive annotation.Especially in documentgrounded dialog systems, human experts need to carefully read the unstructured documents to answer the users' questions.As a result, existing document-grounded dialog datasets are relatively small-scale and obstruct the effective training of dialogue systems.In this paper, we propose an automatic data augmentation technique grounded on documents through a generative dialogue model.The dialogue model consists of a user bot and agent bot that can synthesize diverse dialogues given an input document, which are then used to train a downstream model.When supplementing the original dataset, our method achieves significant improvement over traditional data augmentation methods.We also achieve competitive performance in the low-resource setting.

Practical dialogue systems require robust methods of detecting out-of-scope (OOS) utterances to avoid conversational breakdowns and related failure modes.Directly training a model with labeled OOS examples yields reasonable performance, but obtaining such data is a resource-intensive process.To tackle this limited-data problem, previous methods focus on better modeling the distribution of in-scope (INS) examples.We introduce GOLD as an orthogonal technique that augments existing data to train better OOS detectors operating in low-data regimes.GOLD generates pseudo-labeled candidates using samples from an auxiliary dataset and keeps only the most beneficial candidates for training through a novel filtering mechanism.In experiments across three target benchmarks, the top GOLD model outperforms all existing methods on all key metrics, achieving relative gains of 52.4%, 48.9% and 50.3% against median baseline performance.We also analyze the unique properties of OOS data to identify key factors for optimally applying our proposed method. 1

Derek Chen, Howard Chen, Yi Yang, Alexander Lin, Zhou Yu. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, Yejin Choi. 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.

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.