We study how step-by-step reasoning should be structured for Combinatory Categorial Grammar (CCG) parsing with Large Language Models (LLMs). Whereas most CCG parsers fix the order of parsing decisions—supertagging every word first, then combining the categories bottom-up into a derivation—for LLMs that can generate the full derivation as a single output sequence, this order is an open design question. We fine-tune three open-weight models on four reasoning paths along two axes: parsing direction (top-down or bottom-up) and step format (lexically grounded partial derivations or symbolic derivation steps). Our results show that not all reasoning paths improve CCG parsing. On English CCGbank, only the top-down lexically grounded path consistently outperforms a direct, no-reasoning baseline across all three models. In a zero-shot cross-lingual transfer evaluation from English to German, Italian, and Dutch, the same path outperforms a strong CCG parser baseline, despite trailing it on English. Our findings suggest that LLMs may benefit from a different parsing strategy than classical CCG parsers.

Previously, we introduced a method to generate a multilingual Combinatory Categorial Grammar (CCG) treebank by converting from the Universal Dependencies (UD). However, the method only produces bare CCG derivations without any accompanying semantic representations, which makes it difficult to obtain satisfactory analyses for constructions that involve non-local dependencies, such as control/raising or relative clauses, and limits the general applicability of the treebank. In this work, we present an algorithm that adds semantic representations to existing CCG derivations, in the form of predicate-argument structures. Through hand-crafted rules, we enhance each CCG category with headedness information, with which both local and non-local dependencies can be properly projected. This information is extracted from various sources, including UD, Enhanced UD, and proposition banks. Evaluation of our projected dependencies on the English PropBank and the Universal PropBank 2.0 shows that they can capture most of the semantic dependencies in the target corpora. Further error analysis measures the effectiveness of our algorithm for each language tested, and reveals several issues with the previous method and source data.

This paper introduces an algorithm to convert Universal Dependencies (UD) treebanks to Combinatory Categorial Grammar (CCG) treebanks. As CCG encodes almost all grammatical information into the lexicon, obtaining a high-quality CCG derivation from a dependency tree is a challenging task. Our algorithm relies on hand-crafted rules to assign categories to constituents, and a non-statistical parser to derive full CCG parses given the assigned categories. To evaluate our converted treebanks, we perform lexical, sentential, and syntactic rule coverage analysis, as well as CCG parsing experiments. Finally, we discuss how our method handles complex constructions, and propose possible future extensions.