U ni C onv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues (2020)

Abstract: Building an end-to-end conversational agent for multi-domain task-oriented dialogues has been an open challenge for two main reasons. First, tracking dialogue states of multiple domains is non-trivial as the dialogue agent must obtain complete states from all relevant domains, some of which might have shared slots among domains as well as unique slots specifically for one domain only. Second, the dialogue agent must also process various types of information across domains, including dialogue context, dialogue states, and database, to generate natural responses to users. Unlike the existing approaches that are often designed to train each module separately, we propose “UniConv” - a novel unified neural architecture for end-to-end conversational systems in multi-domain task-oriented dialogues, which is designed to jointly train (i) a Bi-level State Tracker which tracks dialogue states by learning signals at both slot and domain level independently, and (ii) a Joint Dialogue Act and Response Generator which incorporates information from various input components and models dialogue acts and target responses simultaneously. We conduct comprehensive experiments in dialogue state tracking, context-to-text, and end-to-end settings on the MultiWOZ2.1 benchmark, achieving superior performance over competitive baselines.

## introduction a conventional approach to task-oriented dialogues is to solve four distinct tasks: (1) natural language understanding (nlu) which parses user utterance into a semantic frame, (2) dialogue state tracking (dst) which updates the slots and values from semantic frames to the latest values for knowledge base retrieval, (3) dialogue policy which determines an appropriate dialogue act for the next system response, and (4) response generation which generates a natural language sequence conditioned on the dialogue act. this traditional pipeline modular framework has achieved remarkable successes in task-oriented dialogues @xcite @xcite . however, such kind of dialogue system is not fully optimized as the modules are loosely integrated and often not trained jointly in an end-to-end manner, and thus may suffer from increasing error propagation between the modules as the complexity of the dialogues evolves. a typical case of a complex dialogue setting is when the dialogue extends over multiple domains. a dialogue state in a multi-domain dialogue should include slots of all applicable domains up to the current turn (see as the number of dialogue domains increases, traditional pipeline approaches propagate errors from dialogue states to dialogue policy and subsequently, to natural language generator. recent efforts @xcite @xcite address this problem with an integrated sequence-to-sequence structure. these approaches often consider knowledge bases as memory tuples rather than relational entity tables. while achieving impressive performance, these approaches are not scalable to large-scale knowledgebases, e.g. thousands of entities, as the memory cost to query entity attributes increases substantially. another limitation of these approaches is the absence of dialogue act modelling. dialogue act is particularly important in task-oriented dialogues as it determines the general decision towards task completion before a dialogue agent can materialize it into natural language response (see to tackle the challenges in multi-domain taskoriented dialogues while reducing error propagation among dialogue system modules and keeping the models scalable, we propose uniconv, a unified neural network architecture for end-to-end dialogue systems. uniconv consists of a bi-level state tracking (bdst) module which embeds natural language understanding as it can directly parse dialogue context into a structured dialogue state rather than relying on the semantic frame output from an nlu module in each dialogue turn. bdst implicitly models and integrates slot representations from dialogue contextual cues to directly generate slot values in each turn and thus, remove the need for explicit slot tagging features from an nlu. this approach is more practical than the traditional pipeline models as we do not need slot tagging annotation. furthermore, bdst tracks dialogue states in dialogue context in both slot and domain levels. the output representations from two levels are combined in a late fusion approach to learn multi-domain dialogue states. our dialogue state tracker disentangles slot and domain representation learning while enabling deep learning of shared representations of slots common among domains. uniconv integrates bdst with a joint dialogue act and response generator (darg) that simultaneously models dialogue acts and generates system responses by learning a latent variable representing dialogue acts and semantically conditioning output response tokens on this latent variable. the multitask setting of darg allows our models to model dialogue acts and utilize the distributed representations of dialogue acts, rather than hard discrete output values from a dialogue policy module, on output response tokens. our response generator incorporates information from dialogue input components and intermediate representations progressively over multiple attention steps. the output representations are refined after each step to obtain high-resolution signals needed to generate appropriate dialogue acts and responses. we combine both bdst and darg for end-to-end neural dialogue systems, from input dialogues to output system responses. we evaluate our models on the large-scale mul-tiwoz benchmark @xcite , and compare with the existing methods in dst, context-to-text generation, and end-to-end settings. the promising performance in all tasks validates the efficacy of our method. u t : i am also looking for a restaurant in the east slot encoder domain encoder slot self-attn domain self-attn @xmath0. encoder state encoder domain-slot self-attn rnn rnn linear ... <@xmath1. encoder z res+act linear r t z ctx z utt select * from restaurant where @xmath2... 0 1 0 0 .. 1 0 z db b t #entities slot-level dst domain-level dst joint dialogue act and response generator x n s dst x n gen z act linear a t (inform slot i) (request slot j) (dialogue acts) (response) (u 1 , r 1 …,u t-1 , r t-1 ) d: (restaurant, hotel, taxi…) z utt x n d dst b t-1 : @xmath3...<@xmath4...<@xmath5: (<inf_type>, <@xmath6,…) z ctx b t : @xmath7...<@xmath8: there are 8 restaurants in @xmath9. z dst generation-based approaches, our state tracker can incorporate contextual information into domain and slot representations independently. context-to-text generation. this task was traditionally solved by two separate dialogue modules: dialogue policy @xcite and nlg @xcite . recent work attempts to combine these two modules to directly generate system responses with or without modeling dialogue acts. @xcite models action space of dialogue agent as latent variables. @xcite predicts dialogue acts using a hierarchical graph structure with each path representing a unique act. pei et al. (2019); peng et al. (2019) use multiple dialogue agents, each trained for a specific dialogue domain, and combine them through a common dialogue agent. @xcite models dialogue policy and nlg separately and fuses feature representations at different levels to generate responses. our models simultaneously learn dialogue acts as a latent variable while allowing semantic conditioning on distributed representations of dialogue acts rather than hard discrete features. end-to-end dialogue systems. in this task, conventional approaches combine natural language understanding (nlu), dst, dialogue policy, and nlg, into a pipeline architecture @xcite @xcite @xcite @xcite . another framework does not explicitly modularize these components but incorporate them through a sequence-to-sequence framework @xcite @xcite and a memory-based entity dataset of triplets @xcite @xcite @xcite . these approaches bypass dialogue state and/or act modeling and aim to generate output responses directly. they achieve impressive success in generating dialogue responses in open-domain dialogues with unstructured knowledge bases. however, in a task-oriented setting with an entity dataset, they might suffer from an explosion of memory size when the number of entities from multiple dialogue domains increases. our work is more related to the traditional pipeline strategy but we integrate our dialogue models by unifying two major components rather than using the traditional four-module architecture, to alleviate error propagation from upstream to downstream components. different from prior work such as @xcite , our model facilitates multi-domain state tracking and allows learning dialogue acts during response generation. ## method the input consists of dialogue context of t-1 turns, each including a pair of user utterance u and system response r, (u 1 , r 1 ), ..., (u t-1 , r t-1 ), and the user utterance at current turn u t . a taskoriented dialogue system aims to generate the next response r t . the information for responses is typically queried from a database based on the user's provided information i.e. inform slots tracked by a dst. we assume access to a database of all domains with each column corresponding to a specific slot being tracked. we denote the intermediate output, including the dialogue state of current turn b t and dialogue act as a t . we denote the list of all domains @xmath10, d 2 , ...), all slots @xmath11, s 2 , ...), and all acts @xmath12, a 2 , ...). we also denote the list of all (domain, slot) pairs as @xmath13, ds 2 , ...). note that @xmath14. given the current dialogue turn t, we represent each text input as a sequence of tokens, each of which is a unique token index from a vocabulary set v : dialogue context x ctx , current user utterance x utt , and target system response x res . similarly, we also represent the list of domains as x d and the list of slots as x s . in dst, we consider the raw text form of dialogue state of the previous turn b t-1 , similarly as @xcite . in the context-to-text setting, we assume access to the ground-truth dialogue states of current turn b t . the dialogue state of the previous and current turn can then be represented as a sequence of tokens x prev st and x curr st respectively. for a fair comparison with current approaches, during inference, we use the model predicted dialogue states xprev st and do not use x curr st in dst and end-to-end tasks. following @xcite , we consider the delexicalized target response x dl res by replacing tokens of slot values by their corresponding generic tokens to allow learning valueindependent parameters. our model consists of 3 major components (see figure 1 ). first, encoders encode all text input into continuous representations. to make it consistent, we encode all input with the same embedding dimension. secondly, our bi-level state tracker (bdst) is used to detect contextual dependencies to generate dialogue states. the dst includes 2 modules for slot-level and domain-level representation learning. each module comprises attention layers to project domain or slot representations and incorporate important information from dialogue context, dialogue state of the previous turn, and current user utterance. the outputs are combined as a context-aware vector to decode the corresponding inform or request slots in each domain. lastly, our joint dialogue act and response generator (darg) projects the target system response representations and enhances them with information from various dialogue components. our response generator can also learn a latent representation to generate dialogue acts, which condition all target tokens during each generation step.