How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context (2002.00652v2)

Abstract: Recently semantic parsing in context has received considerable attention, which is challenging since there are complex contextual phenomena. Previous works verified their proposed methods in limited scenarios, which motivates us to conduct an exploratory study on context modeling methods under real-world semantic parsing in context. We present a grammar-based decoding semantic parser and adapt typical context modeling methods on top of it. We evaluate 13 context modeling methods on two large complex cross-domain datasets, and our best model achieves state-of-the-art performances on both datasets with significant improvements. Furthermore, we summarize the most frequent contextual phenomena, with a fine-grained analysis on representative models, which may shed light on potential research directions. Our code is available at https://github.com/microsoft/ContextualSP.

An Exploratory Study on Semantic Parsing in Context

The paper "How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context" explores the challenge of semantic parsing when dealing with context-dependent queries. Semantic parsing is pivotal in translating natural language into executable logic forms, such as SQL queries. The complexity increases significantly when user interactions involve context-dependent queries that rely on previous exchanges, a scenario common in dialogues.

Key Contributions

The paper introduces a grammar-based semantic parser designed to handle context in dialogues and adapts various context modeling methods. The researchers evaluate 13 context modeling methods on two complex cross-domain datasets, SParC and CoSQL. The best-performing model achieves state-of-the-art results on both datasets, showcasing improvements over previous benchmarks.

Contextual Phenomena and Methods

The paper explores two main types of contextual phenomena in dialogues: coreference and ellipsis. It highlights the intricacies of coreference, requiring the parser to comprehend references such as pronouns correctly, and the challenges of ellipsis, where questions may be incomplete but gain meaning through preceding context.

The context modeling methods explored include:

• Concatenation: Simple concatenation of recent queries.
• Hierarchical Encoding: Employing turn-level encoders for hierarchical context.
• Copy Mechanisms: Leveraging previous SQL logic forms to assist in parsing.
• Attention Mechanisms: Applying attention over recent contextual inputs.

Each method's performance is scrutinized, revealing that simpler methods like concatenation can be as effective as more complex strategies. The paper identifies strengths and weaknesses across different contextual phenomena.

Numerical Results

The research shows significant improvements in both question match and interaction match metrics. Specifically, the parser exhibits strong performance on turn-by-turn SQL predictions, highlighting its capability to manage flowing dialogue effectively. The integration of BERT further enhances performance, indicating the utility of advanced pre-training techniques in semantic parsing.

Implications and Future Directions

The findings underscore the need for more effective context modeling strategies, particularly in handling complex pronouns and ellipsis. Future work could focus on integrating commonsense reasoning to bolster pronoun resolution effectiveness and refining models to better exploit contextual clues.

The paper serves as a foundation for further research in semantic parsing within interactive systems, encouraging exploration into more nuanced, contextually aware models that leverage both linguistic structures and user interaction patterns.

In sum, the paper contributes a detailed analysis of context modeling within semantic parsing, offering valuable insights into advancing the field and improving the adaptability of machine learning models in real-world dialogue systems.