Learning Language Structures through Grounding

Abstract: Language is highly structured, with syntactic and semantic structures, to some extent, agreed upon by speakers of the same language. With implicit or explicit awareness of such structures, humans can learn and use language efficiently and generalize to sentences that contain unseen words. Motivated by human language learning, in this dissertation, we consider a family of machine learning tasks that aim to learn language structures through grounding. We seek distant supervision from other data sources (i.e., grounds), including but not limited to other modalities (e.g., vision), execution results of programs, and other languages. We demonstrate the potential of this task formulation and advocate for its adoption through three schemes. In Part I, we consider learning syntactic parses through visual grounding. We propose the task of visually grounded grammar induction, present the first models to induce syntactic structures from visually grounded text and speech, and find that the visual grounding signals can help improve the parsing quality over language-only models. As a side contribution, we propose a novel evaluation metric that enables the evaluation of speech parsing without text or automatic speech recognition systems involved. In Part II, we propose two execution-aware methods to map sentences into corresponding semantic structures (i.e., programs), significantly improving compositional generalization and few-shot program synthesis. In Part III, we propose methods that learn language structures from annotations in other languages. Specifically, we propose a method that sets a new state of the art on cross-lingual word alignment. We then leverage the learned word alignments to improve the performance of zero-shot cross-lingual dependency parsing, by proposing a novel substructure-based projection method that preserves structural knowledge learned from the source language.

• The paper introduces techniques like VG-NSL to learn constituency parses from visual data, achieving improved F1 scores using reinforcement learning on image-caption pairs.
• It leverages semantic parsing with program execution and minimum Bayes risk decoding to enhance logic-based language understanding and task generalization.
• It employs cross-lingual dependency parsing via SubDP and multilingual embeddings to effectively transfer structural insights between languages.

Implementing "Learning Language Structures through Grounding" (2406.09662)

The paper "Learning Language Structures through Grounding" explores methods for machine learning systems to acquire language structures by utilizing grounding in non-language modalities, such as vision, program execution results, and cross-lingual data. This response will provide a detailed implementation guide for some key concepts from the paper.

Syntactic Parsing from Visual Grounding

The approach of inducing syntactic structures by grounding language to visual data is facilitated by the Visually Grounded Neural Syntax Learner (VG-NSL). VG-NSL aims to acquire constituency parse trees for sentences using sentence-image pairs by employing a universe of non-terminal and pre-terminal symbols to identify the constituents of a sentence.

Implementation Steps:

1. Constituency Parsing with VG-NSL:
   • Define non-terminal and pre-terminal symbols for parse trees.
   • Implement a parser that induces a binary constituency tree from sentences.
   • Use a neural network to represent semantics of text and its alignment with visual data.
   • Train the model on image-caption pairs to learn parse tree structures.
   • Use reinforcement learning or similar methods to optimize the semantic alignment against visual data.
2. Evaluation with Concreteness Scores:
   • Assign scores for each word token using visual grounding.
   • Adjust model composition based on visual-text alignment scores.
   • Evaluate using $F_1$ score compared against gold parse trees.

Semantic Parsing with Execution Results

The aspect of semantic parsing concerns transforming textual descriptions into logic-based executable programs. This involves handling representations like executable programs encoded in languages like Python and executing them against specific datasets or APIs.

Implementation Steps:

1. Program Representation:
   • Use a domain-specific language that models tasks within the target domain (e.g., mathematical transformations, database operations).
   • Define logical abstractions that represent these tasks.
2. Program Execution as a Learning Signal:
   • Generate candidate logical forms (programs) for a given natural language instruction.
   • Execute these candidate programs on sample data and compare the output against expected results.
   • Use output consistency as a signal to improve parsing accuracy.
3. Decoding with Minimum Bayes Risk:
   • Evaluate alternative program outputs to reduce risk based on execution consistency.
   • Select programs based on execution accuracy, rather than syntactic proximity.
4. Generalization via Multi-Arity Functions:
   • Implement functions in executable templates that can accept multiple arity arguments.
   • Facilitate combinatorial semantics by dynamically invoking these functions.

Cross-Lingual Dependency Parsing

The paper introduces Substructure Distribution Projection (SubDP) for cross-lingual dependency parsing by projecting dependency syntactic structures from one language to another using alignments between languages.

Implementation Steps:

1. Substructure Projection:
   • Use substructures (e.g., dependency arcs) projected between aligned pairs of words in sentences from different languages.
   • Apply pre-trained multilingual embeddings to find alignments.
2. Soft Distribution Use:
   • Translate predicted distributions of substructures from source language into the target language.
   • Train target language parsers using soft label distributions to capture structural likelihoods.
3. Word Alignment Techniques:
   • Utilize models like SimAlign to provide the word alignments that support these projections.
   • Leverage many-to-one alignment information to enhance projection effectiveness.
4. Leveraging Multilingual Representations:
   • Implement models that utilize XLM-R or alike for cross-lingual representation extraction.
   • Consider fine-tuning steps or extraction methods to maximize cross-lingual projection accuracy.

Conclusion

The paper "Learning Language Structures through Grounding" demonstrates the potential for grounding-centered methods to learn linguistic structures in a way that integrates non-linguistic or cross-modal data. Practical application focuses on visual grounding for syntax, programmable logic translation for semantics, and cross-lingual projections for transferring syntactic dependencies.

For each method discussed, detailed implementation guides include constructing data-specific networks that use reinforcement learning, Bayesian techniques, or transfer learning to exploit non-language grounding information consistently. These approaches improve model generalization on unseen structures, leveraging such non-traditional signals to overcome the explicit human annotation cost in supervised paradigms.