Contextual Topic Modeling For Dialog Systems

Accurate prediction of conversation topics can be a valuable signal for creating coherent and engaging dialog systems. In this work, we focus on context-aware topic classification methods for identifying topics in free-form human-chatbot dialogs. We extend previous work on neural topic classification and unsupervised topic keyword detection by incorporating conversational context and dialog act features. On annotated data, we show that incorporating context and dialog acts leads to relative gains in topic classification accuracy by 35% and on unsupervised keyword detection recall by 11% for conversational interactions where topics frequently span multiple utterances. We show that topical metrics such as topical depth is highly correlated with dialog evaluation metrics such as coherence and engagement implying that conversational topic models can predict user satisfaction. Our work for detecting conversation topics and keywords can be used to guide chatbots towards coherent dialog

JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search

An accurate understanding of a user's query intent can help improve the performance of downstream tasks such as query scoping and ranking. In the e-commerce domain, recent work in query understanding focuses on the query to product-category mapping. But, a small yet significant percentage of queries (in our website 1.5% or 33M queries in 2019) have non-commercial intent associated with them. These intents are usually associated with non-commercial information seeking needs such as discounts, store hours, installation guides, etc. In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query. JointMap model works by leveraging the transfer bias that exists between these two related tasks through a joint-learning process. As curating a labeled data set for these tasks can be expensive and time-consuming, we propose a distant supervision approach in conjunction with an active learning model to generate high-quality training data sets. To demonstrate the effectiveness of JointMap, we use search queries collected from a large commercial website. Our results show that JointMap significantly improves both "commercial vs. non-commercial" intent prediction and product category mapping by 2.3% and 10% on average over state-of-the-art deep learning methods. Our findings suggest a promising direction to model the intent hierarchies in an e-commerce search engine.Comment: SIGIR 202

Contextual Dialogue Act Classification for Open-Domain Conversational Agents

Classifying the general intent of the user utterance in a conversation, also known as Dialogue Act (DA), e.g., open-ended question, statement of opinion, or request for an opinion, is a key step in Natural Language Understanding (NLU) for conversational agents. While DA classification has been extensively studied in human-human conversations, it has not been sufficiently explored for the emerging open-domain automated conversational agents. Moreover, despite significant advances in utterance-level DA classification, full understanding of dialogue utterances requires conversational context. Another challenge is the lack of available labeled data for open-domain human-machine conversations. To address these problems, we propose a novel method, CDAC (Contextual Dialogue Act Classifier), a simple yet effective deep learning approach for contextual dialogue act classification. Specifically, we use transfer learning to adapt models trained on human-human conversations to predict dialogue acts in human-machine dialogues. To investigate the effectiveness of our method, we train our model on the well-known Switchboard human-human dialogue dataset, and fine-tune it for predicting dialogue acts in human-machine conversation data, collected as part of the Amazon Alexa Prize 2018 competition. The results show that the CDAC model outperforms an utterance-level state of the art baseline by 8.0% on the Switchboard dataset, and is comparable to the latest reported state-of-the-art contextual DA classification results. Furthermore, our results show that fine-tuning the CDAC model on a small sample of manually labeled human-machine conversations allows CDAC to more accurately predict dialogue acts in real users' conversations, suggesting a promising direction for future improvements.Comment: SIGIR 201

Topic Detection from Conversational Dialogue Corpus with Parallel Dirichlet Allocation Model and Elbow Method

A conversational system needs to know how to switch between topics to continue the conversation for a more extended period. For this topic detection from dialogue corpus has become an important task for a conversation and accurate prediction of conversation topics is important for creating coherent and engaging dialogue systems. In this paper, we proposed a topic detection approach with Parallel Latent Dirichlet Allocation (PLDA) Model by clustering a vocabulary of known similar words based on TF-IDF scores and Bag of Words (BOW) technique. In the experiment, we use K-mean clustering with Elbow Method for interpretation and validation of consistency within-cluster analysis to select the optimal number of clusters. We evaluate our approach by comparing it with traditional LDA and clustering technique. The experimental results show that combining PLDA with Elbow method selects the optimal number of clusters and refine the topics for the conversation

Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize

Building open domain conversational systems that allow users to have engaging conversations on topics of their choice is a challenging task. Alexa Prize was launched in 2016 to tackle the problem of achieving natural, sustained, coherent and engaging open-domain dialogs. In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses. The 2018 competition also included the provision of a suite of tools and models to the competitors including the CoBot (conversational bot) toolkit, topic and dialog act detection models, conversation evaluators, and a sensitive content detection model so that the competing teams could focus on building knowledge-rich, coherent and engaging multi-turn dialog systems. This paper outlines the advances developed by the university teams as well as the Alexa Prize team to achieve the common goal of advancing the science of Conversational AI. We address several key open-ended problems such as conversational speech recognition, open domain natural language understanding, commonsense reasoning, statistical dialog management, and dialog evaluation. These collaborative efforts have driven improved experiences by Alexa users to an average rating of 3.61, the median duration of 2 mins 18 seconds, and average turns to 14.6, increases of 14%, 92%, 54% respectively since the launch of the 2018 competition. For conversational speech recognition, we have improved our relative Word Error Rate by 55% and our relative Entity Error Rate by 34% since the launch of the Alexa Prize. Socialbots improved in quality significantly more rapidly in 2018, in part due to the release of the CoBot toolkit.Comment: 2018 Alexa Prize Proceeding

MIDAS: A Dialog Act Annotation Scheme for Open Domain Human Machine Spoken Conversations

Dialog act prediction is an essential language comprehension task for both dialog system building and discourse analysis. Previous dialog act schemes, such as SWBD-DAMSL, are designed for human-human conversations, in which conversation partners have perfect language understanding ability. In this paper, we design a dialog act annotation scheme, MIDAS (Machine Interaction Dialog Act Scheme), targeted on open-domain human-machine conversations. MIDAS is designed to assist machines which have limited ability to understand their human partners. MIDAS has a hierarchical structure and supports multi-label annotations. We collected and annotated a large open-domain human-machine spoken conversation dataset (consists of 24K utterances). To show the applicability of the scheme, we leverage transfer learning methods to train a multi-label dialog act prediction model and reach an F1 score of 0.79