(Shearing et al., 2018) at AMTA

Translation models demand oodles of training text. In low-resource languages, we need tricks to circumvent the data famine. Languages with many inflected forms for each base word make the famine even scarcer. This paper presents a trick for feeding the hungry by multiplying our few loaves and fish: glossing words and morphological annotations. (Also—my advisor is on this paper :D .)

To help our models out, we want to make the source text more similar to the target.

Shearing et al.’s innovation is the inclusion of morphological glosses, which makes dictionaries more useful. Dictionaries often list only a principal form for a set of words: “run” but not “running” or “ran”. The authors’ glossing process creates in-place translations of “running” and “ran” from the one for “run” and knowledge about verb tense.

The morphological glosses work into a four-stage pipeline that creates an out-of-order English (English-prime; \(E’\)) from the foreign source text. For each foreign word, do the following:

1. Analyze the word to get a lemma and morphological tag: comprábamos → comprar, V;1;PL;PST;IPFV
2. Look up the lemma in a lemma-to-lemma dictionary: comprar → buy
3. Convert the morphological features into a morphological gloss: V;1;PL;PST;IPFV → ‘(we) were VBG.’
4. Replace the placeholder (VBG) with the corresponding form of the English lemma: ‘(we) were VBG’ + buy → ‘(we) were buying’

At the end, you get comprábamos → ‘(we) were buying’, which the authors stick into a phrase-based translation model, turning \(E’\) into \(E\).

Little details:

• Morph analysis using Kann and Schütze (2016).
• Translate lemmas with PanLex or Wiktionary.
• Manually create glosses.
• Inflect English with Spedt and Daelemans (2012).
• The LORELEI data they use gives multiple definitions for some words. Split these into separate lines, expanding your Bitext.
• Append a gloss-to-gloss identity mapping to the bitext, biasing the aligner toward lining these two up.