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Task-oriented Conversational semantic parsing

Sharing three EMNLP 2020 papers on conversational semantic parsing on Watch Party

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Task-oriented Conversational semantic parsing

  1. 1. Jie Cao Dec 04, 2020 *many content are borrowed from the original papers Task-Oriented Conversational Semantic Parsing EMNLP 2020 Watch Party@Amazon Lex
  2. 2. Outlines • Background • Related Works • Recent Advances on representation • Conversational Semantic Parsing[1](Facebook) • Conversational Semantic Parsing for Dialog State Tracking[2](Apple) • Task-oriented Dialogue as Data fl ow Synthesis[3](Microsoft Semantic Machines) • Summary
  3. 3. Background Conventional Task-oriented Dialog System Key Issues on Intent/Slot Fillin g ◦ Poor Scalabilit y ◦ Unseen intent/slot/slot values(even the same domain ) ◦ Lacking knowledge sharing across domai n ◦ Poor Compositionalit y ◦ Complex intent/slot/system ac t ◦ Multiple intent s ◦ Nested intent/slo t Other Issues : ◦ Dialog State Tracking Issue s ◦ Coreferenc e ◦ Multi-domain: slot carryove r ◦ Dialog Polic y ◦ Complex actio n ◦ Low Resource
  4. 4. Related Works A. delexiconlization with semantic dictionaries[4][5] B. neural belief tracker[6][7] C. dual-strategy, generative DST(DST as QA)[8,9,10,11] D. Zero(few)-shot E. …. Better modeling methods ◦ Poor Scalabilit y ◦ Dialog State Tracking Issues
  5. 5. Related Works A. For intent/slot tags • Decomposable multipoint representation for intent/slot names[2,11,12] • Schema-guided dialog (supported with natural language description)[13,14] B. For better Intent/Slot composition A. Hierarchical Representation[1,2,3,15,16] C. Beyond Intent/Slot Representation[3] Better representation design ◦ Poor Scalabilit y ◦ Poor Compositionalit y ◦ Dialog State Tracking/Policy Issue s
  6. 6. Conversational Semantic Parsing[1] (Facebook, SBTOP) • Utterance-level Hierarchical Intent/Slot Representation[15, 16](TOP, TOPV2) Background
  7. 7. Conversational Semantic Parsing[1] (Facebook, SBTOP) Background(utterance-level TOP) Pros: 1. Hierarchical queries 2. Easy Annotation: labeling the span anchors 3. Easy parsing: constituent tree parsing 4. Compatible(following traditional intent/slot framework) Cons: 1. Only utterance level (TOP, TOPV2) 2. In-order constraint 1. Must reconstruct the sentence. 3. Toy dataset: 1. Shallow tree (2.54 avg depth) 2. Short sentences(9 tokens per utterance) 3. Few domains(2 in TOP, 6 new in TOPV2) 4. Limited Composition 1. Support only nested intent, not conjunction for multiple intents.
  8. 8. Conversational Semantic Parsing[1] (Facebook, SBTOP) Limitations of In-order constraint: 1.Discontinuous 2.Strict Word Order 3.Not scalable to Session-based 1.Intent, dialog recovery On Monday, set an alarm for 8am [SL DATETIME 8am on Monday] Solutions: Decouple form •removing all text that does not appear in a leaf slot. •Easy for aggerating for session- based
  9. 9. Conversational Semantic Parsing[1] (Facebook, SBTOP) Session-based hierarchical representation Additional Support for Session-based: extra REF label • Coreferences (REF: EXPLICIT) • Slot-carryover (REF: IMPLICIT) what artist is this ? | this is mozart opus 3 | what movement is this [IN:QUESTION_MUSIC [SL:MUSIC_TYPE movement ] [SL:REF_IMPLICIT [IN:GET_REF [SL:MUSIC_ALBUM_TITLE opus 3 ] ] ] [SL:REF_IMPLICIT [IN:GET_REF [SL:MUSIC_ARTIST_NAME mozart ] ] ] ] Session-based aggregation [IN:QUESTION_MUSIC_ARTIST [SL:MUSIC this]]
  10. 10. Conversational Semantic Parsing[1] (Facebook, SBTOP) Take-aways 1.Main Goal: TOP -> Session-based TOP 2.Main contributions: 1.In-order constraint blocks the session-based: resolved by decouple form 2.Additional Support for Session-based: extra REF label Remaining Issues: 1. Very Poor dataset: 1. Few domains(2 in TOP, 8 in TOPV2, only 4 in SBTOP) 2. Short dialog, as the table 4 statistics 3. Low quality annotation 1. 55% annotator agreement, 94% parsing correct? 2. Limited Composition: 1. Only nested intent, no nested slot 2. No conjunction
  11. 11. Conversational Semantic Parsing for Dialog State Tracking[2](Apple) Main Issues on fl at representation • Poor expressiveness in multiple levels • Intent/slot representation, fl at name tags • Slot value(nested properties) • No conjunctions and nested intents. • Session-based • Coreference/ Slot CarryOver
  12. 12. Conversational Semantic Parsing for Dialog State Tracking[2](Apple) 1. Hierarchical intent/slot names by semantic decoupling I want a fl ight ticket departure at 5 AM tomorrow 1.Context-aware turn-level representatio n • both user and system tur n 2.Non-terminals : • domains: a group of activitie s • verbs: used for a user turn, the verb part of s inten t • actions: used for a system turn, the dialog act to respond the user . • slot s • operators: equals • types: Person, Time, Location . 3. Terminal value nod e • categorical value e.g day of-week • open value (in context, anchored) • reference node
  13. 13. Conversational Semantic Parsing for Dialog State Tracking[2](Apple) 2. Nested/Conjunction properties(e.g time range): slot-operator-(argument1, argument2) argument: 1.sub-slot (time in date, hour in time ) 2.terminal value nod e 1. Canonical categorical label , e.g. day of wee k 3.referece nod e 1.reference to a whole intent(nested intent to fi nd the event fi rst ) 2.reference to co-reference in the previous turn: sub-tree copy
  14. 14. Conversational Semantic Parsing for Dialog State Tracking[2](Apple) Take-aways • Hierarchical in multiple levels • semantic decomposition for intent/slot name • (domain.verb.slot) • slot-operator-(argument1, argument2) • Nested intent(slot-intent), conjunctions • Support nested slots(slot-slot) • Session-based • User-turn-level state and system act, context-aware • Reference to intent • Copy subtree from previous existed state(inline) no (intent-intent) nested cases ? Cons 1. Seems not much semantic for operators now: only “equal” 2. Still focus on intent/slot value state, not a meta computation graph 3. Their experiments didn’t investigate the impact of semantic decomposition 4. Intent/slot name decomposition may be not easy to deploy for large amount of services.
  15. 15. Task-oriented Dialogue as Dataflow Synthesis[3] • At each turn, translate the most recent user utterance into a program (Not a resultant value, or meaning for user utterance). • The predicted program is direct contextual appropriate (executable) response • Predicted programs nondestructively extend the data fl ow graph Beyond Intent-Slot framework ASR TTS Data fl ow Synthesis Generation New Pipelines Common Ground Data fl ow U1 P1 U2 P2 S1 S2 …. U_n P_n S_n
  16. 16. Task-oriented Dialogue as Dataflow Synthesis[3] Reference: refer to previous entity Predicted Program • Solid border: return program value • Refer to some salient previously mentioned node Data- fl ow graph • Shaded node means evaluated • Evaluated node has a dashed result edge • Exception will cause unevaluated nodes dayOfWeek refer Here refer will try to fi nd a previous node with constraints(DataTime type), Here, it is the top-level result of evaluated start node Constraints: Type Constraint: refer(Constraint[Event]()) Property Constraint: refer(Constraint[Event](date= Constraint[DateTime](weekday=thurs))) Role Constraint: (keyword named argument, like slot or subplot) refer(RoleConstraint([date,weekday])).
  17. 17. Task-oriented Dialogue as Dataflow Synthesis[3] Revision: refer to subgraph Predicted Program • Solid border: return program value • Refer to some salient previously mentioned node • Light gray means previous program Data- fl ow graph • Shaded node means evaluated in order • Evaluated node has a dashed result edge • Exception will cause unevaluated nodes Revisie operator take three arguments • rootLoc, a constraint to fi nd the top-level node of the original computation; • oldLoc, a constraint on the node to replace within the original computation; • new, a new graph fragment to substitute there. The fi nal result is the root of revised subgraph, the new start node New nodes will be re-evaluated fi nally Recover is implemented Revision
  18. 18. Task-oriented Dialogue as Dataflow Synthesis[3] Take-aways ASR TTS Data fl ow Synthesis Generation New Pipelines Common Ground Data fl ow U1 P1 U2 P2 S1 S2 …. U_n P_n S_n • Translate the most recent user utterance into a program • Not a resultant value, or meaning for user utterance). • The predicted program is direct contextual appropriate (executable) response • Predicted programs nondestructively extend the data fl ow graph • Graph node are evaluated in order once new predicted program added in • Saving evaluated values for quick reference value • Saving meta graph for revision to subgraphs • Recover and revision
  19. 19. Summary • Previous work are mainly about fl at frame presentation with intent/slot • All three papers are dialog hierarchical presentation (session-based, compositional) • SBTOP and TreeDST follow the intent/slot presentation • While Data fl ow exploit program transformation to translate utterance into program then build data- fl ow graph Symbol Semantic Intent/slot composition Act Session-based Name decomposition n Intent Conjunc tion Slot-intent nested Slot- subslot nested System act Corefere nce Carryover Meta- computation SBTOP N N Y N N Y Y N TreeDST Y Y Y Y Y Y Y N Data fl ow* N Y Y Y Y Y Y Y * Data fl ow are not strictly comparable with intent/slot framework
  20. 20. Q&A Thanks!
  21. 21. References 1. Aghajanyan, Armen, et al. "Conversational Semantic Parsing." Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020. https://www.aclweb.org/anthology/2020.emnlp-main.408.pd

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