Papers › Linguistically-Informed Self-Attention for Semantic Role Labeling

Linguistically-Informed Self-Attention for Semantic Role Labeling

23 Apr 2018EMNLP 2018 10arXiv:1804.08199archive 2025-07-28

Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum

Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. In this work, we present linguistically-informed self-attention (LISA): a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech tagging, predicate detection and SRL. Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates. Syntax is incorporated by training one attention head to attend to syntactic parents for each token. Moreover, if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model. In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art performance for a model using predicted predicates and standard word embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in error. On ConLL-2012 English SRL we also show an improvement of more than 2.5 F1. LISA also out-performs the state-of-the-art with contextually-encoded (ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on out-of-domain text.

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accuracy_np strubell/LISA/src/evaluation_fns_np.py official repository unverified Apache-2.0 (permissive) · e17ea0fc4873cd3e · report
accuracy_tf strubell/LISA/src/evaluation_fns.py official repository unverified Apache-2.0 (permissive) · a8b322b3e51e24de · report
convert_bilou strubell/LISA/src/evaluation_fns_np.py official repository unverified Apache-2.0 (permissive) · 9d94a9256be463e0 · report
convert_conll strubell/LISA/src/evaluation_fns_np.py official repository unverified Apache-2.0 (permissive) · 03c2cf6d3b1daf1b · report
create_metric_variable strubell/LISA/src/evaluation_fns.py official repository unverified Apache-2.0 (permissive) · d5c0e80895ba13a2 · report
get_default strubell/LISA/src/constants.py official repository unverified Apache-2.0 (permissive) · c2f375306505868b · report
get_params strubell/LISA/src/attention_fns.py official repository unverified Apache-2.0 (permissive) · 0e84978dc1139b65 · report
int_to_str_lookup_table strubell/LISA/src/nn_utils.py official repository unverified Apache-2.0 (permissive) · caca64fca95608c5 · report
leaky_relu strubell/LISA/src/nn_utils.py official repository unverified Apache-2.0 (permissive) · ea3b43b22b209a25 · report
lowercase_converter strubell/LISA/src/data_converters.py official repository unverified Apache-2.0 (permissive) · 413d686580cb441b · report
map_strings_to_ints strubell/LISA/src/dataset.py official repository unverified Apache-2.0 (permissive) · c7484be80663fcc6 · report
parse_roots_self_loop_converter strubell/LISA/src/data_converters.py official repository unverified Apache-2.0 (permissive) · deaa9d2fd33c5349 · report
set_vars_to_moving_average strubell/LISA/src/nn_utils.py official repository unverified Apache-2.0 (permissive) · bbf19f387d296223 · report
strip_conll12_domain_converter strubell/LISA/src/data_converters.py official repository unverified Apache-2.0 (permissive) · 15dbbde62d34c21c · report

Tasks

Dependency ParsingMulti-Task LearningPart-Of-Speech TaggingPredicate DetectionSemantic Role LabelingSemantic Role Labeling (predicted predicates)Word Embeddings

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Predicate Detection CoNLL 2005 LISA F1 98.4 #1 of 2 Archive leaderboard report
Predicate Detection CoNLL 2012 LISA F1 97.2 #1 of 1 Archive leaderboard report
Semantic Role Labeling CoNLL 2005 LISA F1 86.04 #12 of 15 Archive leaderboard report
Semantic Role Labeling (predicted predicates) CoNLL 2005 LISA + ELMo F1 86.90 #1 of 5 Archive leaderboard report
Semantic Role Labeling (predicted predicates) CoNLL 2005 LISA F1 84.99 #4 of 5 Archive leaderboard report
Semantic Role Labeling (predicted predicates) CoNLL 2012 LISA + ELMo F1 83.38 #3 of 7 Archive leaderboard report
Semantic Role Labeling (predicted predicates) CoNLL 2012 LISA F1 82.33 #6 of 7 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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