PAIGE : Personalized Adaptive Interactions Graph Encoder for Query Rewriting in Dialogue Systems (2022)

Abstract: Unexpected responses or repeated clarification questions from conversational agents detract from the users’ experience with technology meant to streamline their daily tasks. To reduce these frictions, Query Rewriting (QR) techniques replace transcripts of faulty queries with alternatives that lead to responses thatsatisfy the users’ needs. Despite their successes, existing QR approaches are limited in their ability to fix queries that require considering users’ personal preferences. We improve QR by proposing Personalized Adaptive Interactions Graph Encoder (PAIGE).PAIGE is the first QR architecture that jointly models user’s affinities and query semantics end-to-end. The core idea is to represent previous user-agent interactions and world knowledge in a structured form — a heterogeneous graph — and apply message passing to propagate latent representations of users’ affinities to refine utterance embeddings.Using these embeddings, PAIGE can potentially provide different rewrites given the same query for users with different preferences. Our model, trained without any human-annotated data, improves the rewrite retrieval precision of state-of-the-art baselines by 12.5–17.5% while having nearly ten times fewer parameters.

## introduction facilitating seamless human-computer interactions is a fundamental goal of conversational ai agents such as alexa, cortana, and siri. however, some user interactions lead to frictions, where the ai agent delivers an unexpected response or repeatedly asks the user to clarify the query. such frictions stem from system errors such as automatic speech recognition (asr) and natural language understanding (nlu). some aspects of the frictions are highly personalized, depending on characteristics such as the user's demographics and interests. for example, when asking a conversational agent 1 work done while at amazon alexa ai. to "put on skyfall," one user may expect the system to play a song named "skyfall" while another may wish to see a movie with the same title. query rewriting (qr; @xcite aims to reduce frictions by replacing the transcripts of faulty queries with alternatives that lead to desired responses. personalized qr systems were proposed in @xcite , which restricted rewrite candidates to the particular user's historical requests. such systems, discussed in section 6, typically trained a text encoder to measure the similarity between request and rewrite. while effective, they overlook correlations between requests within a user's dialogue history and the inter-dependencies spanning across other users' interactions. this information can help reformulate defective and ambiguous requests when augmented with external knowledge. to address the aforementioned limitations, we introduce a qr architecture named personalized adaptive interactions graph encoder (paige) that jointly models query semantics, world knowledge, and users' preferences in an end-to-end fashion. the core idea is to represent users' previous interactions in a heterogeneous graph that we can augment with external world knowledge ( §3). the graph representation learning with graph neural networks (gnns) allows us to propagate the representations of users' historical interactions to refine the utterance embeddings in an end-to-end manner through joint training. to construct the heterogeneous graph, we decompose the requests into smaller semantic units such as domains, intents, utterances, entities, and nlu-hypotheses ( §3). we also create nodes representing the users and link every user node to nodes representing entities (e.g., songs, artists) appearing in the user's historical requests. inspired by work in recommendation systems, we use cross-user connections to capture communicative intents among users @xcite and further ground the entity nodes in a knowledge graph (e.g., @xcite , allowing paige to learn from the emerging high-order connectivities. we cast query rewriting as a link prediction problem between an utterance node and nodes corresponding to nlu-hypotheses, that abstract away syntactic variations. once the link to nlu-hypothesis node is predicted, we can follow the graph's edges to select the most frequent non-defective utterance mapping to that nluhypothesis as our rewrite. paige is scalable in training, without the need to load the full graph in memory, and efficient at inference, without the need to re-process the entire graph. our inductive node encoding scheme permits dynamically updating the graph to new knowledge and user interests without model re-training. we demonstrate the efficacy of our system with a detailed analysis of experiments on real-world conversation data ( §5); our contributions are summarized as follows: ## preliminaries spoken dialogue systems consist of many sequential components. when a user interacts with their device, the agent's asr takes the audio signal as input and transcribes it into textual utterance (query). next, the transcript enters the nlu module that interprets it so that the downstream modules can satisfy the user's request. an nlu component typically consists of domain and intent classification and entity linking, executed sequentially. as a preprocessing step for later modules in the dialogue system, the nlu module is instrumental to the system's overall quality. one of the challenges in the nlu module is handling ambiguity or errors cascading from the previous components. query rewriting (qr) component tackles this issue by replacing the asr transcript with an alternative that leads to a satisfactory response for the user. once the nlu pipeline receives a rewrite, regular data flow resumes. ## graph construction the first step is building a heterogeneous graph from user-agent interactions expressed as text and semi-structured metadata. the graph will provide the computational architecture for the message passing algorithm. heterogeneous graph (hg; @xcite ). hg is defined as a directed graph @xmath0, e) with a node type mapping function τ : @xmath1: @xmath2, where each node @xmath3, each link ε ∈ e belongs to a particular relation ψ(ε) ∈ r, |a|+|r| > 2, and if two links belong to the same relation type, the two links have the same starting node types and ending node types. ## paige model computing node representations involves two steps: using specialized encoders to generate nodes' input features and applying message-passing layers to enable the features to interact and coalesce. in the message-passing step, we use relation-specific convolutional modules that aggregate feature vectors of the neighboring nodes. these modules learn to aggregate information from the node's immediate neighborhood, and stacking k such operations effectively convolves features across the k-th order neighborhood, i.e., representations of nodes depend on all the nodes that are at most k edges away. ## experiments here we evaluate paige and empirically validate its performance. we begin with a quantitative assessment on general qr, followed by an evaluation on personalized use-cases. ## limitations paige enables rewrite retrieval from a global set of reformulation candidates but not all defects will be covered by the index. considering this, a generative approach to the problem (roshan-ghias et al., 2020) offers an advantage but generative models pose quality control challenges in production systems, where issues like hallucinations @xcite could have harmful effects. ## conclusion and future work we put forward a graph-based framework for learning user affinities from their interactions with conversational ai agent. the proposed framework learns directly from user feedback and requires no human annotated data. through extensive experiments on real-world conversations, we demonstrate that our proposed paige improves the performance of qr systems and, as a result, reduces friction in users' interactions with the ai agent. defective query roberta rewrite paige rewrite play pop junior clean play pop culture radio clean play pop clean play downtown baby play bad romance play down down baby what is the weather in what is the weather today what is the weather in utah