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# Predicting and Using a Pragmatic Component of Lexical Aspect of Simple Past Verbal Tenses for Improving English-to-French Machine Translation

**Authors:** [Sharid Loáiciga](https://md.mova.elamur.ai/free/en/docs/papers/authors/sharid-loaiciga/), Cristina Grisot · **Year:** 2016 · **Venue:** Linguistic Issues in Language Technology (LiLT), 13(3) **Link:** [aclanthology.org/2016.lilt-13.3](https://aclanthology.org/2016.lilt-13.3/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**5** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/loaiciga-2016-predicting-pragmatic-component-lexical-aspect/) of the 140673 papers whose bibliographies we read[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2016.lilt-13.3/)
- [PDF](https://aclanthology.org/2016.lilt-13.3.pdf)

**Authors (2)**

1. Sharid Loáiciga
2. Cristina Grisot

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

The paper takes the English simple past and asks whether its boundedness (whether the event is seen as reaching a boundary) helps a statistical machine translation system choose the French tense. Two annotators labelled 435 simple-past verbs as bounded or unbounded, using three tests (compatibility with *in* or *for* adverbials, homogeneity, and the progressive entailment test). They agreed on 92% of items, with Cohen's kappa 0.84, and resolved the rest by discussion (kappa 1). Bounded verbs were mostly translated as passé composé or passé simple, and unbounded verbs as imparfait (360 items, 82%). A maximum-entropy classifier predicts boundedness with an accuracy of 0.8857 using manual features, and 0.8224 using only automatically generated features; a random baseline reaches 0.5402. Adding the predicted labels to the translation system gives +0.98 BLEU overall and +1.58 on the sentences with simple-past verbs; a 90% bootstrap interval for the gain is [0.62, 2.85].

## Ideas

- I1. `prag/src/frames.rs` or `en/src/tag.rs`: write the three boundedness tests as rules over the UD tree: an `obl` with the case *in* against *for*, the presence of an internal endpoint, and the `be` plus `-ing` entailment test. Measure rule accuracy on hand-labelled school verbs.
- I2. Adopt the two-annotator protocol: agreement first, then discussion, and report kappa for each round. Keep the discussed items as the gold set for any tense or aspect rule.
- I3. Run a feature ablation as the paper does (remove one feature, retrain, compare). The paper finds that adverbials and the infinitive matter most, so a lexicon-level feature may carry most of the signal; test that with a deterministic perceptron, run twice.

## For Mova

The chapters are [13. Verbs](https://md.mova.elamur.ai/free/en/docs/languages/en/13-verbs/) (tense and aspect) and [30. Pragmatics and discourse: overview](https://md.mova.elamur.ai/free/en/docs/languages/en/30-pragmatics-overview/). The code is `en/src/tag.rs` for the features, `prag/src/frames.rs` for the verb reading, and `world/src/events.rs` for the event types. The measures: the rule accuracy of the three tests against gold, the kappa of a two-pass annotation, and the ablation table. The machine translation part is not Mova's goal; the boundedness labels are the part to borrow.

Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work.

```
@misc{loaiciga2016predicting,
  title = {{Predicting and Using a Pragmatic Component of Lexical Aspect of Simple Past Verbal Tenses for Improving English-to-French Machine Translation}},
  author = {Loáiciga, Sharid and Grisot, Cristina},
  year = {2016},
  url = {https://aclanthology.org/2016.lilt-13.3/}
}
```

- [← previous GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains](https://md.mova.elamur.ai/free/en/docs/papers/liu-2024-gdtb-genre-diverse-data-english/)
- [next → What is it? Disambiguating the different readings of the pronoun 'it'](https://md.mova.elamur.ai/free/en/docs/papers/loaiciga-2017-what-is-it-disambiguating-different/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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