Let's do it "again": A First Computational Approach to Detecting Adverbial Presupposition Triggers
What it is about
The task is to predict, from the preceding discourse, whether an adverbial presupposition trigger (too, again, also, still, yet) belongs on a given head word. The adverb is removed and the model sees up to 50 tokens before the head plus the rest of the sentence. Positive samples come from the Penn Treebank and Gigaword. Negatives are contexts with the same governor but no adverb, so the governor alone gives no clue; the excess sense of too (governor an adjective or adverb, «too far») is filtered out. A logistic regression on bigrams is barely above chance except for too and yet. A BiLSTM with a parameter-free self-attention pooling (weighted pooling over a token-by-token matching matrix) reaches 76% on PTB and 82% on Gigaword also, but only about 60% on again, still and the pooled set. The analysis shows that the cue is a recurring event or entity earlier in the text («continue» licensing still).
Ideas
- I1. Mova's presupposition licensing in
prag/src/infer.rs(same predicate within 50 tokens, after this paper) can be measured as a classifier on the paper's task. Build the dataset the same way from open text (the Gutenberg tales in the database): positives are sentences with again/still/also/too/yet attached to a verb, the adverb removed; negatives have the same governor lemma without the adverb. Report accuracy of «licensed = predict trigger» per adverb against the most-frequent-class baseline. The governor-matched negatives make the control honest. - I2. Extend the licence check from "same predicate" to the paper's cues: a coreferent entity (via
corefchains or the frame resolver), an aspectual verb (continue, keep → still), and a prior negated event (→ yet, again). Measure each cue's contribution on the I1 set withPRAG_OFF-style switches. - I3. Use the same check in generation. When the family's dialogue layer (
world/src/talk.rs) or a lesson summary repeats an event already in the discourse memory with the same agent and verb, insert again; when a second agent does the same, insert too. The licensing explanation becomes the reason for the word. Test that a first mention never gets again.
For Mova
33. Implicature and presupposition §6.1–6.3 (triggers, what Mova computes, licensing); prag/src/infer.rs (presuppositions), world/src/talk.rs; measure: per-adverb accuracy on the governor-controlled set against the majority baseline.
Cite
@inproceedings{cianflone2018let,
title = {{Let's do it "again": A First Computational Approach to Detecting Adverbial Presupposition Triggers}},
author = {Cianflone, Andre and Feng, Yulan and Kabbara, Jad and Cheung, Jackie Chi Kit},
booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
year = {2018},
doi = {10.18653/v1/p18-1256},
eprint = {1806.04262},
archivePrefix = {arXiv},
url = {https://doi.org/10.18653/v1/p18-1256}
}References
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