Bootstrapping Lexical Choice via Multiple-Sequence Alignment
<|reference_start|>Bootstrapping Lexical Choice via Multiple-Sequence Alignment: An important component of any generation system is the mapping dictionary, a lexicon of elementary semantic expressions and corresponding natural language realizations. Typically, labor-intensive knowledge-based methods are used to construct the dictionary. We instead propose to acquire it automatically via a novel multiple-pass algorithm employing multiple-sequence alignment, a technique commonly used in bioinformatics. Crucially, our method leverages latent information contained in multi-parallel corpora -- datasets that supply several verbalizations of the corresponding semantics rather than just one.   We used our techniques to generate natural language versions of computer-generated mathematical proofs, with good results on both a per-component and overall-output basis. For example, in evaluations involving a dozen human judges, our system produced output whose readability and faithfulness to the semantic input rivaled that of a traditional generation system.<|reference_end|>
@article{barzilay2002bootstrapping,
    title={Bootstrapping Lexical Choice via Multiple-Sequence Alignment},
    author={Regina Barzilay and Lillian Lee},
    journal={arXiv preprint arXiv:cs/0205065},
    year={2002},
    archivePrefix={arXiv},
    eprint={cs/0205065},
    primaryClass={cs.CL}
}