Parallel Sentence Mining by Constrained Decoding

By Pinzhen Chen, Nikolay Bogoychev, Kenneth Heafield, Faheem Kirefu. 2020

23 citations recorded by the source corpus.

We present a novel method to extract parallel sentences from two monolingual corpora, using neural machine translation. Our method relies on translating sentences in one corpus, but constraining the decoding by a prefix tree built on the other corpus. We argue that a neural machine translation system by itself can be a sentence similarity scorer and it efficiently approximates pairwise comparison with a modified beam search. When benchmarked on the BUCC shared task, our method achieves results comparable to other submissions.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Eneko Agirre
2. Thien Huu Nguyen
3. Dan Roth
4. Xianpei Han
5. Jialong Tang
6. Meng Liao
7. Oscar Sainz
8. Yaojie Lu

Citations in the corpus

• Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021, methodology): “To exploit the event schema knowledge, we pro-pose to employ a trie-based constrained decoding algorithm (Chen et al., 2020a; Cao et al., 2021) for event generation.”
• Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey (2021, methodology): “To improve the model, a trie-based constrained decoding procedure [15, 30] is introduced to ensure the generation of valid event structures.”