Language to Logical Form with Neural Attention (1601.01280v2)

Abstract: Semantic parsing aims at mapping natural language to machine interpretable meaning representations. Traditional approaches rely on high-quality lexicons, manually-built templates, and linguistic features which are either domain- or representation-specific. In this paper we present a general method based on an attention-enhanced encoder-decoder model. We encode input utterances into vector representations, and generate their logical forms by conditioning the output sequences or trees on the encoding vectors. Experimental results on four datasets show that our approach performs competitively without using hand-engineered features and is easy to adapt across domains and meaning representations.

• The paper introduces neural attention in semantic parsing by developing Seq2Seq and Seq2Tree models that reduce reliance on handcrafted features.
• It demonstrates that the Seq2Tree variant captures hierarchical structures effectively, achieving competitive accuracy on four diverse datasets.
• The study leverages dynamic argument identification to handle rare entities, enhancing cross-domain applicability in natural language understanding.

Language to Logical Form with Neural Attention

"Language to Logical Form with Neural Attention" by Li Dong and Mirella Lapata addresses the challenge of translating natural language utterances into logical form representations using an attention-enhanced encoder-decoder model. This problem, known as semantic parsing, is critical for applications such as question-answering systems and interacting with knowledge bases. Traditional approaches to semantic parsing often rely heavily on hand-crafted features and domain-specific templates, which can limit their adaptability and scalability.

Approach

The proposed method leverages a general encoder-decoder framework utilizing recurrent neural networks (RNNs) with long short-term memory (LSTM) units. This model is designed to bridge natural language and logical forms while minimizing manual feature engineering. The authors introduce two main architectures within this framework:

1. Sequence-to-Sequence (Seq2Seq) Model: This model treats both the input and output as sequences. It encodes the input sentence into vectors and decodes them into logical forms. The Seq2Seq model is augmented with an attention mechanism to dynamically focus on different parts of the input while generating each token in the output sequence.
2. Sequence-to-Tree (Seq2Tree) Model: This variant generates logical forms in a tree structure, reflecting their inherent hierarchical nature. The tree decoder generates tokens in a top-down manner, better capturing the compositional structure of logical forms.

Attention Mechanism

An attention mechanism is adopted to learn soft alignments between the input and output sequences. This mechanism allows the model to weigh the relevance of different parts of the input sentence dynamically, providing context to the decoder at each step. The attention scores are computed based on the dot product between the encoder's hidden states and the current hidden state of the decoder. The resulting context vector, which is a weighted sum of the encoder states, helps in predicting the output tokens more accurately.

Evaluation

The approach is evaluated on four datasets: Jobs, Geo, Atis, and Ifttt. The datasets cover a range of domains and logical form complexities:

• Jobs: Queries related to job listings paired with Prolog-style logical forms.
• Geo: Questions about U.S. geography paired with lambda-calculus expressions.
• Atis: Flight booking queries with lambdas-calculus-based representations.
• Ifttt: User-crafted if-this-then-that recipes represented as abstract syntax trees.

The model's performance is compared against several baselines and previous state-of-the-art systems. Results demonstrate that the attention-enhanced Seq2Seq and Seq2Tree models achieve competitive or superior accuracy across all datasets. Notably, the Seq2Tree model consistently outperforms the Seq2Seq model, particularly in datasets with nested logical forms, exemplifying the importance of capturing hierarchical structures.

Key Results and Implications

The Seq2Tree model achieves an accuracy of 90.0% on the Jobs dataset, 87.1% on Geo, and 84.6% on Atis, showing improvements over many prior systems. The use of attention mechanisms and argument identification significantly enhances performance. Argument identification, in particular, addresses the challenge of rare or unseen entities and numbers by dynamically substituting them with type names and unique IDs during both training and inference phases.

Future Directions

The research opens several avenues for future work:

• Unsupervised Learning: Developing models that learn from question-answer pairs without access to annotated logical forms could increase the applicability of semantic parsing models.
• Cross-Domain Generalization: Further exploration of the model's adaptability to diverse and unseen domains.
• Advanced Attention Mechanisms: Incorporating multi-head attention or transformer-based architectures could yield better alignment and context understanding.

In conclusion, the paper presents a robust and generalizable framework for semantic parsing that minimizes dependency on domain-specific features. The combination of sequence and tree-based decoding with attention mechanisms offers a compelling approach validated by strong empirical results across multiple benchmarks.