An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation (2022)

Abstract: We study the interpretability issue of task-oriented dialogue systems in this paper. Previously, most neural-based task-oriented dialogue systems employ an implicit reasoning strategy that makes the model predictions uninterpretable to humans. To obtain a transparent reasoning process, we introduce neuro-symbolic to perform explicit reasoning that justifies model decisions by reasoning chains. Since deriving reasoning chains requires multi-hop reasoning for task-oriented dialogues, existing neuro-symbolic approaches would induce error propagation due to the one-phase design. To overcome this, we propose a two-phase approach that consists of a hypothesis generator and a reasoner. We first obtain multiple hypotheses, i.e., potential operations to perform the desired task, through the hypothesis generator. Each hypothesis is then verified by the reasoner, and the valid one is selected to conduct the final prediction. The whole system is trained by exploiting raw textual dialogues without using any reasoning chain annotations. Experimental studies on two public benchmark datasets demonstrate that the proposed approach not only achieves better results, but also introduces an interpretable decision process.

## introduction neural task-oriented dialogue systems have enjoyed a rapid progress recently @xcite @xcite , achieving strong empirical results on various benchmark datasets such as smd @xcite and multi-woz @xcite . however, most existing approaches suffer from the lack of explainability due to the black-box nature of neural networks @xcite @xcite , which may hurt the trustworthiness between the users and the system. for the context entity (i.e., leichhardt) and answer entity (i.e., cityroom) are marked as red and yellow, respectively. the triple containing the context entity and answer entity is not directly stored in kb and should be derived by a reasoning chain formed by multiple kb triplets. ## related work task-oriented dialogue traditionally, taskoriented dialogue systems are built via pipelinebased approaches where task-specific modules are designed separately and connected to generate system responses @xcite @xcite @xcite . in another spectrum, many works have started to shift towards end-to-end approaches to reduce human efforts @xcite @xcite @xcite . lei et al. (2018) propose a twostage sequence-to-sequence model to incorporate dialogue state tracking and response generation jointly in a single sequence-to-sequence architecture. zhang et al. (2020) propose a domain-aware multi-decoder network (damd) to combine belief state tracking, action prediction and response generation in a single neural architecture. most recently, the success of large-scale pre-trained language models (e.g., bert, gpt-2) @xcite has spurred a lot of recent dialogue studies starting to explore large-scale pre-trained language model for dialogues @xcite . in task-oriented dialogue, @xcite use gpt-2 to fine-tune on multiwoz dataset for dialogue response generation. peng et al. (2020) and hosseini-asl et al. (2020) employed a single unified gpt-2 model jointly trained for belief state prediction, system action and response generation in a multi-task fashion. however, most existing approaches cannot explain why the model makes a specific decision in a human understandable way. we aim to address this limitation and introduce interpretability for dialogue reasoning in this study. neuro-symbolic reasoning neuro-symbolic reasoning has attracted a lot of research attentions recently due to its advantage of exploiting the representational power of neural networks and the compositionality of symbolic reasoning for more robust and interpretable models @xcite @xcite @xcite @xcite . the main difference between neuro-symbolic vs. pure neural networks lies in how the former combines basic rules or modules to model complex functions. @xcite propose a neuro-symbolic model that can jointly learn subsymbolic representations and interpretable rules from data via standard back-propagation. in visual qa, @xcite propose neural module networks to compose a chain of differentiable modules wherein each module implements an operator from a latent program. @xcite propose to discover symbolic program trace from the input question and then execute the program on the structured representation of the image for visual question answering. however, these approaches cannot be easily adapted to task-oriented dialogues due to the error propagation issue caused by multihop reasoning on large-scale kbs. thus, we aim to bridge this gap by developing a neuro-symbolic approach for improving task-oriented dialogues. ## preliminary in this work, we focus on the problem of taskoriented dialogue response generation with kbs. formally, given the dialogue history x and knowledge base b, our goal is to generate the system responses y word-by-word. the probability of the generated responses can be written as: ## neuro-symbolic reasoning for task-oriented dialogue to compute the kb distribution p kb,t , we present two novel modules: hypothesis generator (hg) and hierarchical reasoning engine (hre). we take the context vector c (equation 3 ) as the input of hg module and generate k hypotheses h, each of which are then fed into the hre module to generate the logical reasoning chains and their belief scores. the estimated belief scores are then served as p kb,t , giving us a distribution over the entities in the kb. next, we describe how each component works in detail and explain how they interact with each other for generating p kb,t . bert x1 x2 xm …… hcls h1 h2 hm …… [cls] shared feature transformation layer structure prediction query states prediction candidates prediction hypothesis synthetization ℍ gumbel-softmax gumbel-softmax target hypothesis sub-hypothesis leaf node leaf node kb kb kb min-max pooling belief scores "𝑆𝑂𝑆" …… ℎ !"#,% & ℎ !"#,% 𝑦 % ℎ !"#,' ℎ ()* 𝑝 +", 𝐾𝐵 𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 𝑉𝑜𝑐𝑎𝑏𝑢𝑙𝑎𝑟𝑦 𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 ×(1 -𝑝 !"# ) ×𝑝 !"# 𝐹𝑖𝑛𝑎𝑙 𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 (a) hypothesis generator -phase (i) (c) dialogue encoding (b) hierarchical reasoning engine-phase (ii) (d) response generation 𝑃 -.#/0 ,1 𝑃 20 ,1 𝑃(𝑤) kb: [h1, r1, t1] [h2, r2, t2] …… [hn, rn, tn] @xmath0,1 , ℎ !"#,1 & ] h i head entity r i t i relation tail entity kb sub-hypothesis ## conclusion in this paper, we propose an explicit and interpretable neuro-symbolic kb reasoning framework for task-oriented dialogue generation. the hypothesis generator employs a divide-and-conquer strategy to learn to generate hypotheses, and the reasoner employs a recursive strategy to learn to generate verification for the hypotheses. we evaluate our proposed framework on two public benchmark datasets including smd and multiwoz 2.1. extensive experimental results demonstrate the effectiveness of our proposed framework, as well being more interpretable.