How much do word embeddings encode about syntax?

How much do word embeddings encode about syntax? Jacob Andreas and Dan KleinUC Berkeley

Everybody loves word embeddings few most that the each a this every [Collobert 2011] [Collobert 2011, Mikolov 2013, Freitag 2004, Schuetze 1995, Turian 2010]

What might embeddings bring? Mary Cathleen complained about the magazine’s shoddy editorial quality . average executive

Three hypotheses Vocabulary expansion(good for OOV words) Statistic pooling(good for medium-frequency words) Embedding structure(good for features) Cathleen Mary average editorial executive tense transitivity

Cathleen Mary Vocabulary expansion: Embeddings help handling of out-of-vocabulary words

Vocabulary expansion Cathleen yellow Mary John enormous Pierre hungry

Vocabulary expansion Mary Cathleen complained about the magazine’s shoddy editorial quality. Cathleen yellow Mary John enormous Pierre hungry

Vocab. expansion results +OOV Baseline

Vocab. expansion results (300 sentences) +OOV Baseline

average editorial executive Statistic pooling hypothesis: Embeddings help handling ofmedium-frequency words

Statistic pooling {NN} {NN, JJ} editorial executive {JJ} giant {NN} kind {NN, JJ} average

Statistic pooling {NN, JJ} {NN, JJ} editorial executive {JJ, NN} giant {NN, JJ} kind {NN, JJ} average

Statistic pooling editorial NN {NN} {NN, JJ} editorial executive {JJ} giant {NN} kind {NN, JJ} average editorial NN

Statistic pooling results +Pooling Baseline

Vocab. expansion results (300 sentences) +Pooling Baseline

tense transitivity Embedding structure hypothesis: The organization of the embedding spacedirectly encodes useful features

Embedding structure “transitivity” vanishing dined dining vanished “tense” devoured devouring assassinated assassinating VBD dined VBD dined [Huang 2011]

Embedding structure results +Features Baseline

Embedding structure results (300 sentences) +Features Baseline

To summarize (300 sentences)

Combined results +OOV +Pooling Baseline

Vocab. expansion results (300 sentences) +OOV +Pooling Baseline

What about… • Domain adaptation? (no significant gain) • French? (no significant gain) • Other kinds of embeddings? (no significant gain)

Why didn’t it work? • Context clues often provide enough information to reason around words with incomplete / incorrect statistics • Parser already has a robust OOV, smallcount models • Sometimes “help” from embeddings is worse than nothing: bifurcate Soap homered Paschi tuning unrecognized

What about other parsers? • Dependency parsers(continuous repr. as syntactic abstraction) • Neural networks(continuous repr. as structural requirement) [Henderson 2004, Socher 2013] [Henderson 2004, Socher 2013, Koo 2008, Bansal 2014]

Conclusion • Embeddings provide no apparent benefit to state-of-the-art parser for: • OOV handling • Parameter pooling • Lexicon features • Code online at http://cs.berkeley.edu/~jda

How much do word embeddings encode about syntax?