# Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation

## TL;DR

This paper introduces a domain adaptation method for CCG parsing that automatically generates CCG corpora from dependency trees, significantly improving parser performance across diverse domains.

## Contribution

The paper presents a simple, parser-architecture-independent method for domain adaptation by automatically generating CCG data from dependency resources.

## Key findings

- Significant performance improvements on speech conversation and math problem datasets.
- Effective domain adaptation demonstrated across four different datasets.
- Method is compatible with current top-performing CCG parsers.

## Abstract

We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees. Our solution is conceptually simple, and not relying on a specific parser architecture, making it applicable to the current best-performing parsers. We conduct extensive parsing experiments with detailed discussion; on top of existing benchmark datasets on (1) biomedical texts and (2) question sentences, we create experimental datasets of (3) speech conversation and (4) math problems. When applied to the proposed method, an off-the-shelf CCG parser shows significant performance gains, improving from 90.7% to 96.6% on speech conversation, and from 88.5% to 96.8% on math problems.