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Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition

Summary

Phrase meaning often cannot be built by simply combining word meanings. Composition can change what a word means (meaning shift, as in guilt trip) or add unstated content that needs world knowledge (implicit meaning, as in olive oil vs. baby oil). Shwartz and Dagan (TACL, 2019) test how well current text representations handle both effects.

They built an evaluation suite of six classification tasks covering MWEs and free word combinations. The tasks reuse existing annotated datasets, recast in a common format. Where the original data had no context, they added Wikipedia sentences, and they used lexically disjoint splits to limit memorization. Generic classifiers were trained on top of six pre-trained static and contextualized representations. Human performance was estimated on annotated test samples.

Contextualized embeddings outperform static ones overall. On meaning-shift tasks they roughly match human performance. On recovering implicit meaning they remain clearly below humans. The authors argue that a language-modeling objective alone may not be enough to capture implicit meaning. The released suite gives future representation research a reusable benchmark for lexical composition.

Key points

  • Six tasks, with train/val/test sizes:
    • VPC classification (Tu and Roth 2012), 919/209/220.
    • LVC classification (Tu and Roth 2011), 1521/258/383.
    • NC Literality (Reddy et al. 2011, augmented with Tratz 2011), 2529/323/138.
    • NC Relations (SemEval 2013 Task 4), 1274/162/130.
    • AN Attributes (HeiPLAS), 837/108/106.
    • Phrase Type tagging (STREUSLE), 3017/372/376.
  • Source data: the VPC data has 1,348 BNC sentences covering 23 phrasal verbs built from six frequent verbs. The SemEval data contains 356 noun compounds with 12,446 human-written paraphrases.
  • Added contexts: for out-of-context datasets, sentences of 15–20 words were taken from a January 2018 English Wikipedia dump. Data was split about 80/10/10 under lexical constraints, for example by verb, head noun, or adjective.
  • Representations tested: static word2vec, GloVe and fastText, and contextualized ELMo, OpenAI GPT and BERT.
  • Availability: data and code are released at github.com/vered1986/lexcomp.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

56 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 47 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl_a_00277

@article{shwartz2019still,
  title = {{Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition}},
  author = {Shwartz, Vered and Dagan, Ido},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.1162/tacl\_a\_00277},
  url = {https://doi.org/10.1162/tacl_a_00277}
}

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