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Question-Answer Driven Semantic Role Labeling

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Question-Answer Driven Semantic Role Labeling Question-Answer Driven Semantic Role Labeling Using Natural Language to Annotate Natural Language Luheng He, Mike Lewis, Luke Zettlemoyer University of Washington EMNLP 2015 1 Semantic Role Labeling (SRL) who did what to whom, when and where? 2 Semantic Role Labeling (SRL) Role Predicate Argument Agent Patent Time They increased the rent drastically this year Manner 2 Semantic Role Labeling (SRL) Role Predicate Argument Agent Patent Time They increased the rent drastically this year Manner • Defining a set of roles can be difficult • Existing formulations have used different sets 2 Existing SRL Formulations and Their Frame Inventories FrameNet PropBank 1000+ semantic frames, 10,000+ frame files 10,000+ frame elements (roles) with predicate-specific roles Frame: Change_position_on_a_scale This frame consists of words that indicate the Roleset Id: rise.01 , go up change of an Item's position on a scale Arg1-: Logical subject, patient, thing rising (the Attribute) from a starting point Arg2-EXT: EXT, amount risen (Initial_value) to an end point (Final_value). Arg3-DIR: start point The direction (Path) … Arg4-LOC: end point Lexical Units: Argm-LOC: medium …, reach.v, rise.n, rise.v, rocket.v, shift.n, … Unified Verb Index, University of Colorado http://verbs.colorado.edu/verb-index/ PropBank Annotation Guidelines, Bonial et al., 2010 FrameNet II: Extended theory and practice, Ruppenhofer et al., 2006 FrameNet: https://framenet.icsi.berkeley.edu/ 3 This Talk: QA-SRL • Introduce a new SRL formulation with no frame or role inventory • Use question-answer pairs to model verbal predicate-argument relations • Annotated over 3,000 sentences in weeks with non-expert, part-time annotators • Showed that this data is high-quality and learnable 4 Our Annotation Scheme Given sentence and a verb: They increased the rent this year . 5 Our Annotation Scheme Given sentence and a verb: They increased the rent this year . Step 1: Ask a question about the verb: Who increased something ? 5 Our Annotation Scheme Given sentence and a verb: They increased the rent this year . Step 1: Ask a question Step 2: Answer with words about the verb: in the sentence: Who increased something ? They 5 Our Annotation Scheme Given sentence and a verb: They increased the rent this year . Step 1: Ask a question Step 2: Answer with words about the verb: in the sentence: Who increased something ? They Step 3: Repeat, write as many QA pairs as possible … 5 Our Annotation Scheme Given sentence and a verb: They increased the rent this year . Step 1: Ask a question Step 2: Answer with words about the verb: in the sentence: Who increased something ? They Step 3: Repeat, write as many QA pairs as possible … What is increased ? the rent When is something increased ? this year 5 Previous Method: Annotation with Frames ARG1 ARG2 amount??? risen ARG4 end??? point The rent rose 10% from $3000 to $3300 ARG3 start??? point Frameset: rise.01 , go up • Depends on pre-defined frame inventory Arg1-: Logical subject, patient, • thing rising Annotators need to: Arg2-EXT: EXT, amount risen 1) Identify the Frameset Arg3-DIR: start point 2) Find arguments in the sentence Arg4-LOC: end point 3) Assign labels accordingly Argm-LOC: medium • If frame doesn’t exist, create new The Proposition Bank: An Annotated Corpus of Semantic Roles, Palmer et al., 2005 http://verbs.colorado.edu/propbank/framesets-english/rise-v.html 6 Our Method: Q/A Pairs for Semantic Relations ARG1 ARG2 amount??? risen ARG4 end??? point The rent rose 10% from $3000 to $3300 ARG3 start??? point Wh-Question Answer What rose ? the rent How much did something rise ? 10% What did something rise from ? $3000 What did something rise to ? $3300 Comparing to Existing SRL Formulations No Role Inventory! Large Role Inventory Question Role (Verbal) Answer Predicate Argument Predicate Question-Answer Driven SRL SRL (QA-SRL) 8 Advantages • Easily explained • No pre-defined roles, few syntactic assumption • Can capture implicit arguments • Generalizable across domains 9 Advantages • Easily explained • No pre-defined roles, few syntactic assumption • Can capture implicit arguments • Generalizable across domains • Only modeling verbs (for now) Limitations • Not annotating verb senses directly • Can have multiple equivalent questions 9 Advantages • Easily explained • No pre-defined roles, few syntactic assumption • Can capture implicit arguments • Generalizable across domains • Only modeling verbs (for now) Limitations • Not annotating verb senses directly • Can have multiple equivalent questions Challenges • What questions to ask? • Quality - Can we get good Q/A pairs? • Coverage - Can we get all the Q/A pairs? 9 Outline • Semantic Role Labeling Motivation and Intuition • Our Method: QA-SRL • Annotation Task Design Data Collection and Analysis • Dataset Statistics • Quality Analysis Learning Tasks and Baselines Future Work and Conclusion 10 Question-Answer Driven SRL Given sentence s, target verb v Annotate all possible question-answer pairs <q,a> 11 Question-Answer Driven SRL Given sentence s, target verb v Annotate all possible question-answer pairs <q,a> • Question q should start with a wh-word and contain the target verb v • Answer a should be a phrase from the sentence s. Multiple correct answers are allowed. 11 Writing Questions q WH AUX SBJ TRG OBJ1 PP OBJ2 2 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ 12 Writing Questions q WH AUX SBJ TRG OBJ1 PP OBJ2 2 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ WH: Who, What, When, Where, Why, How, How much 12 Writing Questions q WH AUX SBJ TRG OBJ1 PP OBJ2 2 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ WH: Who, What, When, Where, Why, How, How much AUX: Auxiliary verbs, including negations. i.e. is, might, wo n’t 12 Writing Questions q WH AUX SBJ TRG OBJ1 PP OBJ2 2 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ WH: Who, What, When, Where, Why, How, How much AUX: Auxiliary verbs, including negations. i.e. is, might, wo n’t SBJ, OBJ1, OBJ2: someone, something, do something, etc. 12 Writing Questions q WH AUX SBJ TRG OBJ1 PP OBJ2 2 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ WH: Who, What, When, Where, Why, How, How much AUX: Auxiliary verbs, including negations. i.e. is, might, wo n’t SBJ, OBJ1, OBJ2: someone, something, do something, etc. TRG: Target verb, including inflected forms. 12 Writing Questions q WH AUX SBJ TRG OBJ1 PP OBJ2 2 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ WH: Who, What, When, Where, Why, How, How much AUX: Auxiliary verbs, including negations. i.e. is, might, wo n’t SBJ, OBJ1, OBJ2: someone, something, do something, etc. TRG: Target verb, including inflected forms. PP: Preposition. i.e. to, for, from, about, etc. 12 Writing Questions WH* AUX SBJ TRG* OBJ1 PP OBJ2 Who built something What had someone said When was someone expected to do something Where might something rise from 13 Annotation Interface 14 Annotation Interface 14 Dataset Statistics newswire (PropBank) Wikipedia 11000 8250 8,109 5500 2750 3,336 1,241 0 Sentences Verbs QA Pairs 15 Dataset Statistics newswire (PropBank) Wikipedia 11000 10,798 8250 8,109 5500 4,440 2750 3,336 1,959 1,241 0 Sentences Verbs QA Pairs 15 Cost and Speed newswire Wikipedia 1.6 $1.57 1.2 0.8 0.4 $0.58 0 Cost per Verb Cost per Sentence • Part-time freelancers from upwork.com (hourly rate: $10) • ~2h screening process for native English proficiency 16 Cost and Speed newswire Wikipedia 1.6 $1.57 1.2 0.8 $1.01 $0.58 0.4 $0.45 0 Cost per Verb Cost per Sentence • Part-time freelancers from upwork.com (hourly rate: $10) • ~2h screening process for native English proficiency 16 Cost and Speed newswire Wikipedia 9 1.6 9min $1.57 6.75 1.2 4.5 6min 0.8 $1.01 2.25 $0.58 0.4 $0.45 0 Cost per Verb Cost per Sentence Time per Sentence • Part-time freelancers from upwork.com (hourly rate: $10) • ~2h screening process for native English proficiency 16 Sample Annotation Sentence: Clad in his trademark black velvet suit , the soft- spoken clarinetist announced that . and that it was his mother ’s birthday , so he was going to play her favorite tune from the record . PropBank QA-SRL (CoNLL-2009) Who would play something ? ARG0: he the soft-spoken clarinetist / he What would be played ? ARG1: tune her favorite tune from the record When would someone play something? / his mother ’s birthday 17 Sample Annotation Sentence: Clad in his trademark black velvet suit , the soft- spoken clarinetist announced that . and that it was his mother ’s birthday , so he was going to play her favorite tune from the record . PropBank QA-SRL (CoNLL-2009) Who would play something ? match ARG0: he the soft-spoken clarinetist / he What would be played ? match ARG1: tune her favorite tune from the record When would someone play something? / precision his mother ’s birthday loss 17 Agreement with PropBank: Results Precision Recall 90 86.3 81.4 67.5 45 22.5 0 All Roles Core Roles Adjuncts Core Roles: A0-A5 Adjuncts: ADV, CAU,DIR, EXT, LOC, MNR, PNC, PRD, TMP 18 Agreement with PropBank: Results Precision Recall 90 86.3 89.8 81.4 85.9 67.5 45 22.5 0 All Roles Core Roles Adjuncts Core Roles: A0-A5 Adjuncts: ADV, CAU,DIR, EXT, LOC, MNR, PNC, PRD, TMP 18 Agreement with PropBank: Results Precision Recall 90 86.3 89.8 81.4 85.9 67.5 59.9 63.6 45 22.5 0 All Roles Core Roles Adjuncts Core Roles: A0-A5 Adjuncts: ADV, CAU,DIR, EXT, LOC, MNR, PNC, PRD, TMP 18 Inter-Annotator Agreement • QA Equivalence: Same wh-word + Overlapping answers • Agreed QA Pairs: Proposed by at least 2 of the 5 annotators 19 Inter-Annotator Agreement •• QAAgreed Equivalence: QA pairs Same by five wh-word annotators: + Overlapping 2.6-2.8 answers QA/verb •• AgreedOne annotator QA Pairs: can Proposed recover: by 2.2-2.3at least 2QA/verb of the 5 annotators (80%) 19 Wh-words vs.
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