Semantic Parsing for Task Oriented Dialog using Hierarchical Representations (1810.07942v1)

Abstract: Task oriented dialog systems typically first parse user utterances to semantic frames comprised of intents and slots. Previous work on task oriented intent and slot-filling work has been restricted to one intent per query and one slot label per token, and thus cannot model complex compositional requests. Alternative semantic parsing systems have represented queries as logical forms, but these are challenging to annotate and parse. We propose a hierarchical annotation scheme for semantic parsing that allows the representation of compositional queries, and can be efficiently and accurately parsed by standard constituency parsing models. We release a dataset of 44k annotated queries (fb.me/semanticparsingdialog), and show that parsing models outperform sequence-to-sequence approaches on this dataset.

Semantic Parsing for Task Oriented Dialog Using Hierarchical Representations

The paper entitled "Semantic Parsing for Task Oriented Dialog Using Hierarchical Representations" explores innovative methodologies in the field of natural language processing, particularly within task-oriented dialog systems. The research is rooted in the development and application of a hierarchical annotation scheme adept at capturing the semantics of complex, compositional queries. This paper proposes the Task Oriented Parsing (TOP) representation, which aims to transcend the constraints associated with traditional intent-slot frameworks by introducing a hierarchy reminiscent of constituency parsing models.

Traditional task-oriented systems often rely on segregated intent-slot tags, which are insufficient for representing queries with intricate compositions involving multiple intents and nested structures. The authors address this limitation by deploying a hierarchical annotation that enhances expressive capacity without intensifying the complexity of annotation or parsing processes—challenges often encountered with logical forms or dependency graphs. Their dataset, consisting of 44,783 annotated queries, predominantly encompasses navigation and event-related utterances, and this corpus forms the basis for evaluating the effectiveness of the hierarchical model.

Contributions and Robust Quantitative Insights

Key contributions of this paper include the definition of the hierarchical semantic representation capable of modeling nested and compositional queries, thus providing a more flexible and expressive schema compared to existing flat models. They provide a publicly accessible dataset of over 44,000 annotated requests, ensuring high coverage and inter-annotator agreement, indicative of the practical scalability of their approach.

The authors implemented various models to evaluate the proposed system's potential. Notably, they used Recurrent Neural Network Grammars (RNNG), which successfully leveraged its inductive bias to outperform robust sequence-to-sequence (seq2seq) baselines on metrics like the exact match and $F_1$ scores. Quantitatively, the RNNG achieved an exact match accuracy of 78.51% and demonstrated consistent outperformance against seq2seq architectures, such as CNN-based and Transformer-based models. The latter, despite their capacity to transduce sequences effectively, fell short on maintaining tree validity and producing syntactic representations necessary for executing task-specific dialogues.

Implications and Speculation on Future Developments

The findings emphasize the viability of syntactic structures like the proposed hierarchical model in enhancing the flexibility and interpretability of intelligent dialog systems. This paper posits a strategic trade-off between expressiveness and learnability, as their tree structures offer optimal coverage without introducing excessive annotation complexity. This positions the model as an intermediary between the cumbersome logical forms and the rudimentary intent-slot systems.

Future initiatives may capitalize on these insights to further refine semantic representations for dialog systems, potentially integrating additional graph-based or hybrid methodologies to handle those rare complex queries that currently lie beyond the TOP representation’s capabilities. Furthermore, advancements in convolutional architectures or Transformer models could benefit from integrating hierarchical or tree-based processing techniques to optimize their performance in parsing tasks leaning heavily toward linguistic structure, especially given that top-$k$ prediction accuracy vastly improved at higher beam sizes.

Ultimately, this work underscores the potential for hierarchical semantic parsing in task-oriented dialogs and provides a robust foundation for subsequent inquiry and technological development within conversational AI. As systems increasingly encounter multifaceted user queries, integrating such representation schemes could enhance the efficacy, adaptability, and user satisfaction in automated dialog systems.