A large annotated corpus of entailments and contradictions
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1 A large annotated corpus of entailments and contradictions Samuel R. Bowman Gabor Angeli Christopher Potts Christopher D. Manning Stanford University Premise: A man speaking or singing into a microphone while playing the piano. Hypothesis: A man is performing surgery on a giraffe while singing. Label: contradiction
2 Corpus Semantics and Entailments
3 Corpora for semantics? Answering questions about what sentences people are willing or able to say: Almost any corpus of naturally occurring text, like... CoCA BNC The Web
4 Corpora for semantics? Answering questions about how people interpret particular structures: Collecting new human judgments Corpora annotated with meanings: (sentence, context, meaning)
5 Semantic annotations How should the meaning be represented? Metalanguage sentences Logical forms: Catch 22. Nonlinguistic data: Images or videos Difficult to study statistically States of affairs in a game (cf. Potts, 2012) Very limited domain Object language sentences: Natural language inference corpora!
6 Natural language inference (NLI) also known as recognizing textual entailment (RTE) Premise: James Byron Dean refused to move without blue jeans Hypothesis: James Dean didn t dance without pants Label: {entails, contradicts, neither} Example: MacCartney thesis 09
7 NLI in Semantics Logical forms make predictions about entailments. If blue_jeans = λx. pants(x) denim(x) blue(x) and pants = λx. pants(x) then The dog is wearing blue jeans. entails The dog is wearing pants....assuming reasonable logical forms for the rest of the words in the sentence. cf. research Natural Logic: A logic that isolates entailment predictions without using explicit logical forms for sentences.
8 NLI in Semantics Correctly predicting entailments requires capturing every aspect of semantics except grounding: Lexical entailment: Does jeans entail pants? Quantification: Does most N V entail some N V? Factivity and implicativity Does N managed to V entail N Ved? Object and event coreference Lexical ambiguity and scope ambiguity Propositional attitudes Modality...
9 Existing NLI Corpora
10 Natural language inference data Corpus Size Full Sentences Expert Curated Model Biased FraCaS.3k RTE 7k SICK 10k DG 728k ~ Levy 1,500k PPDB 100,000k
11 Natural language inference data Corpus Size Full Sentences Expert Curated Model Biased FraCaS.3k RTE 7k SICK 10k SNLI 570k DG 728k ~ Levy 1,500k PPDB 100,000k
12 The Stanford NLI Corpus The Stanford Natural Language Inference Corpus (SNLI) 570k pairs of sentences Collected over Amazon Mechanical Turk Simple annotation guidelines No model necessary
13 Coreference issues for NLI data collection
14 One event or two? Premise: A boat sank in the Pacific Ocean. Hypothesis: A boat sank in the Atlantic Ocean.
15 One event or two? One. Premise: A boat sank in the Pacific Ocean. Hypothesis: A boat sank in the Atlantic Ocean. Label: Contradiction
16 One event or two? One. Premise: Ruth Bader Ginsburg was appointed to the US Supreme Court. Hypothesis: I had a sandwich for lunch today Label: Contradiction
17 One event or two? Two. Premise: Ruth Bader Ginsburg was appointed to the US Supreme Court. Hypothesis: I had a sandwich for lunch today Label: Neutral
18 One event or two? Two. Premise: A boat sank in the Pacific Ocean. Hypothesis: A boat sank in the Atlantic Ocean. Label: Neutral (two different events) No contradictions at all in typical natural sentences.
19 Our data collection prompt
20 Source captions from Flickr30k: Young, Lai, Hodosh, and Hockenmaier, TACL 14
21 Entailment Source captions from Flickr30k: Young, Lai, Hodosh, and Hockenmaier, TACL 14
22 Entailment Neutral Source captions from Flickr30k: Young, Lai, Hodosh, and Hockenmaier, TACL 14
23 Entailment Neutral Contradiction Source captions from Flickr30k: Young, Lai, Hodosh, and Hockenmaier, TACL 14
24 One photo or two? One. Premise: Ruth Bader Ginsburg being appointed to the US Supreme Court. Hypothesis: A man eating a sandwich for lunch. Label: Different photo ( contradiction)
25 Flickr30k descriptive captions The source of the premise in almost every* SNLI pair: Premise: A white dog with long hair jumps to catch a red and green toy. Hypothesis: An animal is jumping to catch an object. Label: Entailment Flickr30k: Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier, TACL 14 Collected over Mechanical Turk *A small subset of the data draws on visualgenome.org data from a pilot study.
26 What we got
27 Some sample results Premise: Two women are embracing while holding to go packages. Entailment: Two woman are holding packages.
28 Some sample results Premise: A man in a blue shirt standing in front of a garage-like structure painted with geometric designs. Neutral: A man is repainting a garage
29 Some sample results Premise: A man selling donuts to a customer during a world exhibition event held in the city of Angeles Contradiction: A woman drinks her coffee in a small cafe.
30 General observations Entailments aren t logically strict, and incorporate commonsense background assumptions. Only see semantic phenomena which can occur in scene descriptions. (Hard to avoid.) Premise and hypothesis sentences can be syntactically (and stylistically) very different. Spelling and grammar errors are rare. (NB: the corpus is not cleaned) Contradiction hypotheses are often (but not always) somehow related to their premises.
31 Sentence length
32 Bare NPs vs. full sentences Data source Premises (Flickr30k) Hypotheses (our work) % full sentences (Stanford Parser S )
33 Data validation
34 Data validation Premise: Two women are embracing while holding to go packages. Entailment: Two woman are holding packages.
35 Data validation Premise: Two women are embracing while holding to go packages. Entailment: Two woman are holding packages. Labels: entailment
36 Data validation Premise: Two women are embracing while holding to go packages. Entailment: Two woman are holding packages. Labels: entailment entailment entailment neutral entailment
37 Data validation Premise: Two women are embracing while holding to go packages. Entailment: Two woman are holding packages. Labels: entailment entailment entailment neutral entailment Gold label: entailment
38 Data validation Premise: Two women are embracing while holding to go packages. Entailment: Two woman are holding packages. Labels: entailment entailment entailment neutral entailment Gold label: entailment Relabeled 10% of SNLI.
39
40 The results Condition % of pairs 5 vote unanimous agreement: 58.3% 3-4 vote majority for one label including author: 32.9% 3-4 vote majority for one label not including original author: 6.8% No majority for any one label: 2.0%
41 Give it a try! Available now (with an accompanying paper): nlp.stanford.edu/projects/snli Standard distribution includes JSON and tab-separated text. Sentences are included both raw and tokenized+parsed with Stanford CoreNLP.
42 Thanks! Download the corpus: nlp.stanford.edu/projects/snli More questions? We gratefully acknowledge support from a Google Faculty Research Award, a gift from Bloomberg L.P., the Defense Advanced Research Projects Agency (DARPA) Deep Exploration and Filtering of Text (DEFT) Program under Air Force Research Laboratory (AFRL) contract no. FA , the National Science Foundation under grant no. IIS , and the Department of the Navy, Office of Naval Research, under grant no. N Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of Google, Bloomberg L.P., DARPA, AFRL NSF, ONR, or the US government. Our MTurk workers were amazing, and we thank them too.
43 Validation: Other metrics % of total labels matching gold label 89.0% % of total labels matching author s label 85.8% Fleiss κ entailment 0.72 contradiction 0.77 neutral 0.60 overall 0.70
44 Realities of Mechanical Turk Employed ~2,500 workers Used several worker qualification strategies, and bonuses in validation Reviewed and discarded ~100 pairs that were marked as problematic (either in collection or validation). Data entry errors (early sumbission) Bad source captions from Flickr30k ( The image didn t load ) Uninterpretable English Updated FAQ ~10 times to discourage overly regular data Caught ~20 cases of fraud Mostly random guessing on the validation task Reddit (HWTF) reviews extremely positive: Many found task fun
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