📄 Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions

What it is about

The paper models a pair of sentences as a sequence of tree edits between their dependency trees. Edits are insert, delete and relabel operations plus three that handle reordering: move a subtree, make a new root, and move a sibling. The sequence is found by greedy best-first search, using a normalised partial tree kernel as the heuristic, and capped at 200 iterations (400 for RTE). A logistic regression over 33 integer features, mostly counts of edit types and of the nodes they touch, classifies the pair. The model reaches 62.8% accuracy on RTE-3, against 64.3% for the best cited hybrid system, 73.2% on the paraphrase corpus, and a MAP of 0.6091 on TREC answer selection, the best reported for that task at the time. Each pair takes about five seconds.

Ideas

• I1. A move-subtree operation in the tree-diff code of tools/roundtrip: the paper's example "lives near Rossville Blvd" needs three edits without a move and one with it. Count the edits per pair before and after, on the school lessons.
• I2. Use the 33 count features as an explainable classifier: each weight belongs to an edit type a teacher can read, such as "a subject was removed". Fit the weights on a training split, then print the top contributions for every decision.
• I3. Check that the search is deterministic by fixing the enumeration order in a BTreeMap. The paper reports that the order had no effect on the transformations; Mova should confirm this by running twice on the same pairs.
• I4. Negative control: shuffle the words of the hypothesis. Edit counts should rise and the classifier should drop, otherwise the features are not measuring the pair.

For Mova

The relevant chapters are languages/en/21-clause-structure.md and languages/en/36-discourse-relations.md. The code is tools/roundtrip (tree-to-tree comparison) and prag/src/infer.rs. The measures are the edit count per pair, the classifier accuracy on a held-out split, and the negative control.

Cite

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

@inproceedings{heilman2010tree,
  title = {{Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions}},
  author = {Heilman, Michael and Smith, Noah A.},
  booktitle = {Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
  year = {2010},
  url = {https://aclanthology.org/N10-1145/}
}

References

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