Neural Belief Tracker: Data-Driven Dialogue State Tracking

By Nikola Mrksic, Diarmuid Ó Séaghdha, Tsung-Hsien Wen, Blaise Thomson, Susannah Young. 2016

509 citations recorded by the source corpus.

One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user’s goal at every step of the dialogue. However, most current approaches have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted lexicons for capturing some of the linguistic variation in users’ language. We propose a novel Neural Belief Tracking (NBT) framework which overcomes these problems by building on recent advances in representation learning. NBT models reason over pre-trained word vectors, learning to compose them into distributed representations of user utterances and dialogue context. Our evaluation on two datasets shows that this approach surpasses past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

The Natural Language Decathlon: Multitask Learning as Question Answering (2018, methodology): “Most models use a structured approach [Mrkšić et al., 2016], with the most recent work making use of both global and local modules to learns representations of the user utterance and previous system actions [Zhong et al., 2018].”
Continual Prompt Tuning for Dialog State Tracking (2022, methodology): “Traditional ontology-based classification methods (Mrkšić et al., 2017; Lee et al., 2019) require access to all candidate values.”
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling (2018, background): “The corpus was later extended to additional two languages for cross-lingual research (Mrkˇsi´c et al., 2017b).”
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems (2018, background): “…components for natural language understanding (NLU) (Mesnil et al., 2015; Liu and Lane, 2016; Hakkani-T¨ur et al., 2016), dialogue state tracking (DST) (Henderson et al., 2014c; Mrkˇsi´c et al., 2016), and dialogue policy learning (Gasic and Young, 2014; Shah et al., 2016; Su et al., 2016, 2017).”
Challenges in Building Intelligent Open-domain Dialog Systems (2019, background): “e user [47,145,152]. 2A task schema typically defines a set of user intents, and for each intent defines a set of dialog acts, slot-value pairs. 3Dialog management performs both dialog state tracking [42,81] and response selection via policy [64,91,123,172]. 4Recently, there are end-to-end methods [”
A Survey of Cross-lingual Word Embedding Models (2017, methodology): “Other striking findings include P@1 scores for Russian-Ukrainian (0.84 vs. 0.59), Czech-Slovak (0.82 vs. 0.59), Serbian-Croatian (0.78 vs. 0.56), or Danish-Norwegian (0.73 vs. 0.67).”
Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management (2017, background): “…language understanding module (Chen et al., 2016; Yang et al., 2017), a dialogue belief state tracker (Henderson et al., 2014; Perez and Liu, 2016; Mrkˇsi´c et al., 2017) to predict user intent and track the dialogue history, a dialogue policy (Young et al., 2013; Gaˇsi´c and Young, 2014;…”
Iterative policy learning in end-to-end trainable task-oriented neural dialog models (2017, background): “ialog systems are designed with complex pipelines [4, 5, 6, 7], and there are usually separately developed modules for natural language understanding (NLU) [8, 9, 10, 11], dialog state tracking (DST) [12, 13], and dialog management (DM) [14, 15, 16]. Such pipeline approach inherently make it hard to”
A Survey on Dialogue Systems: Recent Advances and New Frontiers (2017, background): “[59] proposed a neural belief tracker (NBT) to detect the slot-value pairs.”
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems (2019, background): “Mrkˇsi´c et al. (2017) use distributional representation learning to leverage semantic information from word embeddings to and resolve lexical/morphological ambiguity.”
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models (2021, methodology): “…is to maintain an accurate account of the dialog belief state (i.e., information slots and their values provided by the user) at each turn of the conversation, combining the information from the current user utterance and the conversation history (Henderson et al., 2014; Mrkˇsi´c et al., 2017).”
Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context (2019, background): “The trend of building task-oriented systems is changing from training separate dialog models independently (Young et al. 2013; Wen et al. 2015; Liu and Lane 2016; Mrkˇsi´c et al. 2017) to end-to-end trainable dialog models (Zhao et al. 2017; Wen et al. 2017; Eric and Manning 2017).”
ConveRT: Efficient and Accurate Conversational Representations from Transformers (2019, background): “They mitigate the requirements for explicit task-specific semantics hand-crafted by domain experts (Henderson et al., 2014; Mrkšić et al., 2015, 2017). large collection of responses in a joint semantic space, and then retrieve the most relevant response by matching the query representation…”
Multi-task Learning for Joint Language Understanding and Dialogue State Tracking (2018, methodology): “Mrkˇsi´c et al. 2017 proposed the NBT model which eliminates the LU step by directly operating on the user ut-terance.”
TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking (2020, methodology): “Dialogs of such complex nature are tough for traditional approaches that rely on the availability of a candidate list due to scalability and generalization issues (Mrkˇsi´c et al., 2016; Liu and Lane, 2017; Ramadan et al., 2018; Rastogi et al., 2017).”