Improving Intent Classification in an E -commerce Voice Assistant by Using Inter-Utterance Context (2020)

Abstract: In this work, we improve the intent classification in an English based e-commerce voice assistant by using inter-utterance context. For increased user adaptation and hence being more profitable, an e-commerce voice assistant is desired to understand the context of a conversation and not have the users repeat it in every utterance. For example, let a user’s first utterance be ‘find apples’. Then, the user may say ‘i want organic only’ to filter out the results generated by an assistant with respect to the first query. So, it is important for the assistant to take into account the context from the user’s first utterance to understand her intention in the second one. In this paper, we present our approach for contextual intent classification in Walmart’s e-commerce voice assistant. It uses the intent of the previous user utterance to predict the intent of her current utterance. With the help of experiments performed on real user queries we show that our approach improves the intent classification in the assistant.

## introduction recently, there has been a notable advancement in the field of voice assistants 1 . consequently, voice assistants are being deployed heavily in lucrative domains such as e-commerce @xcite , customer service @xcite and healthcare @xcite . there are various ecommerce based voice assistants available in the market, including amazon's alexa voice shopping @xcite ) and walmart's on google assistant/siri 2 . their goal is to free us from the tedious task of buying stuff by visiting stores and websites. a major challenge in the fulfilment of this goal is their capability to precisely understand an utterance in a dialog without providing much context 1 https://bit.ly/2xr71xv 2 https://bit.ly/33tmwkn , https://bit.ly/2jqh9eo in the utterance. for example, an assistant must precisely understand that when a user says 'five' after a query to add bananas to her cart then she intents to add five bananas to her cart. whereas if a user says 'five' as her first utterance to the shopping assistant then her intention is unknown (i.e., it does not represent any e-commerce action at the start of a conversation). handling such scenarios require the natural language understanding (nlu) component in the assistants to utilize the context while predicting the intent associated with an utterance. the current intent prediction systems @xcite @xcite do not focus on such contextual dependence. in this work we integrate inter-utterance contextual features in the nlu component of the shopping assistant of walmart company to improve its intent classification. there are four main aspects of a voice assistant, namely, speech-to-text, nlu, dialog management (dm) and text-to-speech. the nlu component (such as the one in liu and lane ( 2016 )) identifies intent(s) and entities in a user utterance. the dialog manager uses the output of the nlu component to prepare a suitable response for the user. the nlu systems in the currently available voice enabled shopping assistants 3 do not focus on inter-utterance context and hence the onus of context disambiguation lies upon the dialog manager. although it is possible to capture a small number of such cases in the dialog manager, it becomes difficult for it to scale for large number of contextually dependent utterances. for example, let us consider the user utterance 'five' after the utterance to add something to her cart. then a dialog manager can predict its intent by using the rule: if previous @xmath0. but such a general rule can not be created for many other queries such as 'organic please' (previous @xmath1, @xmath2, @xmath3. in this paper, we present our work to improve the intent classification in the shopping assistant of walmart company by using inter-utterance context. our work also reduces the contextual disambiguation burden from the dialog manager. here, we implement our approach using two neural network based architectures. with the help of experiments we also compare the two implementations. ## related work various intent classification works @xcite @xcite have been proposed in the recent years. most of them mainly focus on the current utterance only and try to predict its intent based on the information present in it. we take it one step further and use the context from the immediately previous utterance. a work which focuses on the contextual information while predicting the intents is mentioned in @xcite . it uses visual information as context. although it is useful in an assistant which comes with a screen, it is not applicable for a voice only assistant. another work @xcite uses an entire conversation (intents of all the utterances and all the replies from the assistant) as context. although it makes sense to use the entire conversation as context in a general purpose chat-bot, we believe that in the e-commerce domain a conversation between an assistant and a user is fragmented into smaller goals such as 'finding an item in the inventory' and 'adding an item in cart'. each such goal should be defined by a small number of utterances only. this is because the objective of the voice assistant is to simplify the shopping experience for the user instead of engaging her in a long conversation for a simpler task. so, in our work we use the intent of the previous utterance only as the context for the intent prediction of the current utterance. ## data generation & our approach in this section we provide the data generation details and a detailed overview of our context aware intent classification implementations. ## evaluation & results the main goal of this work is to improve the intent classification in the nlu component of walmart's shopping assistant by using inter-utterance context. in this section we present the quantitative details of the data, the experiments and the analysis of the results. ## conclusion & future work in this paper, we presented our work of improving intent classification in walmart's shopping assistant. we used previous utterance's intent as context to identify current utterance's intent. the contextual update in the nlu layer (intent classification) also takes the burden of intent based contextual disambiguation away from a dialog manager. as hypothesized, the experimental results show that our approach improves the intent classification by handling contextual queries. we presented two implementations of our approach and compared them with respect to live user logs. though, in this work our main focus was on the contextual disambiguation of intents, the entities are also contextually dependent. for example 'five' uttered after 'add bananas' may refer to the quantity five whereas if uttered after 'pick a delivery time' may refer to the time of day five (am/pm). in future we would like to use contextual features to disambiguate between entities and improve the entity tagging as well.