Break It Down: A Question Understanding Benchmark (2001.11770v1)

Abstract: Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the ordered list of steps, expressed through natural language, that are necessary for answering a question. We develop a crowdsourcing pipeline, showing that quality QDMRs can be annotated at scale, and release the Break dataset, containing over 83K pairs of questions and their QDMRs. We demonstrate the utility of QDMR by showing that (a) it can be used to improve open-domain question answering on the HotpotQA dataset, (b) it can be deterministically converted to a pseudo-SQL formal language, which can alleviate annotation in semantic parsing applications. Last, we use Break to train a sequence-to-sequence model with copying that parses questions into QDMR structures, and show that it substantially outperforms several natural baselines.

Break It Down: A Question Understanding Benchmark

The paper "Break It Down: A Question Understanding Benchmark" introduces a novel approach to natural language question understanding by focusing on decomposing questions into structured meaning representations termed Question Decomposition Meaning Representation (QDMR). This representation captures the ordered list of reasoning steps required to compute answers to compositional and complex questions, offering a structured and formal yet natural language interface for question understanding tasks across multiple domains.

Key Contributions

1. QDMR Formalism: Inspired by database query languages like SQL and semantic parsing techniques, QDMR is introduced as a formalism to represent the steps necessary to decompose complex questions, agnostic to the information source. It allows questions from various modalities such as databases, texts, and images to be uniformly interpreted in terms of abstract reasoning steps.
2. Break Dataset: The authors present the "Break" dataset, which includes 83,978 annotated question-decomposition pairs derived from ten datasets spanning three different modalities: structured databases, unstructured textual data, and visual data. This dataset is aimed at serving as a benchmark for question decomposition tasks in research.
3. Crowdsourcing Pipeline: The paper details a scalable crowdsourcing approach to annotate QDMR structures and validates the ability to train non-expert annotators to produce high-quality outputs. This methodology underscores the feasibility of generating large-scale annotations through guided crowdsourcing efforts.
4. Applications of QDMR: The utility of QDMR is demonstrated through improvements in open-domain question answering on the HotpotQA dataset. By leveraging QDMR for multi-step reasoning and semantic parsing, improvements in model performance are illustrated, with substantial gains noted in IR (Information Retrieval) metrics when decomposed questions are used for context retrieval.
5. Neural QDMR Parser Development: A sequence-to-sequence model with copy mechanisms is employed to parse questions into QDMR representations. The parser significantly outperforms baseline methods, showcasing the potential for automated question decomposition using machine learning.

Implications and Speculation

QDMR formalism and the Break dataset have important implications for advancing the field of NLP, particularly in bridging the gap between human-like understanding of questions and machine reasoning. By providing a formal structure to decomposing complex questions, the paper enhances the ability of systems to perform multi-step reasoning required for effective question answering. This could influence the design of AI systems capable of interacting more naturally with humans by understanding and addressing queries across multiple domains seamlessly.

Future developments in AI could build on these foundations, integrating QDMR in broader conversational AI systems or employing it to facilitate more efficient semantic parsing pipelines, thereby potentially reducing the requirement for highly domain-specific training data. Additionally, the application of QDMR to understand user queries in real-time systems, such as virtual assistants, might lend robustness and interpretive clarity that is currently challenging with existing models.

Overall, the paper’s contribution provides a pivotal benchmark for evaluating question understanding in NLP, reinforcing the need for structured query representations that can handle complex reasoning patterns, and setting the stage for further exploration in automating detailed and insightful responses to human inquiries.