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Natural Language Processing for Human-Computer Interaction

Natural Language Processing for Human-Computer Interaction. Hae-Chang Rim Korea University. Contents. Introduction Conversational Natural Language Interface Language Understanding Components Dialog Management Models Conclusion. Introduction. What is Natural Language Processing (NLP)?

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Natural Language Processing for Human-Computer Interaction

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  1. Natural Language Processing for Human-Computer Interaction Hae-Chang Rim Korea University

  2. Contents • Introduction • Conversational Natural Language Interface • Language Understanding Components • Dialog Management Models • Conclusion

  3. Introduction What is Natural Language Processing (NLP)? Two motivations for NLP Research fields of NLP NLP and HCI

  4. Introduction • What is Natural Language Processing (NLP)? • This is a difficult question to answer since “there are almost as many definitions as there are researchers studying it” (Obermeier, 1988) The branch of information science thatdeals with natural language information The formulation and investigation of computationally effective mechanisms for communication through natural language A subfield of artificial intelligence and linguistics for making computers "understand" statements written in human languages • Two motivations for NLP (Allen, 1994) • Thescientific or linguistic motivation is to understand the nature of language through the tools provided by computer science • The technological motivation is to improve communication between humans and machines

  5. Introduction • Research fields of NLP Sentence Level Morphological Analysis Syntactic Analysis Semantic Analysis Discourse Level Discourse Structure Analysis Speech Act Recognition Reference Resolution Dialog Level Dialog Management Planning & Reasoning Language Generation Application Level Information Retrieval Question Answering Conversational Agent And many other things …

  6. Introduction • NLP and Human Computer Interaction (HCI) • Goals of HCI (Bill 98) • Developing systems which match or augment the physical, perceptual, and cognitive capabilities of users • Investigating a way to ensure the user-friendliness and robustness of interactive computer systems • NLP for HCI • Since natural language is the most effortless and effective way of communication in human-human interaction– either spoken or typewritten, it may effectively complement other available modalities • Sometimes, natural language may even be the only applicable modality: • When driving car, carrying a baggage … • NLP offers mechanisms for incorporating natural language knowledge and modalities into user interfaces (Bill 98)

  7. Conversational Natural Language Interface What is conversational NL interface system? Limitation of current NLP techniques Typical architecture of dialog system

  8. Conversational NL interface • What is conversational natural language interface system (i.e. Dialog system)? • Systems providing an interface that permits interaction through natural language between the user and a computer-based application Hi, I’d like to fly to Seattle Tuesday morning. Ok. Let’s see, I have a United flights .. That’s OK Will you return to Pittsburgh from …

  9. Conversational NL interface • Some limitations of current NLP techniques • Full natural language understanding by machine may not be realized in the near future • Difficulty of resolving ambiguity of natural language • Lack of resources … • Practical Dialog Hypothesis (Allen et al. 01) • Because of the limitation, current dialog systems have usually been developed in a specific domain and for a specific task under the practical dialog hypothesis • Hypothesis: • “The conversational competence required for practical dialogues, while still complex, is significantly simpler to achieve than general human conversational competence”

  10. Conversational NL interface • Typical Architecture of Dialog System Language Understanding Components Speech Recognition QA Agents Dialog Management Component Task Agents Other Agents Natural Language Generation

  11. Conversational NL interface • Two important issues related to NLP in building a dialog system Language Understanding Components How to understand user’s utterance? How to manage dialog between user and system? Dialog Management Component

  12. Language Understanding Components 1. Overview of language understanding process 2. Morphological analysis 3. Part of speech (POS) tagging 3. Syntactic parsing 4. Semantic analysis 5. Discourse analysis

  13. Language Understanding Components • The aim of language understanding components • Analyze user’s utterance with discourse context and transform it to semantic structure which the machine can understand • Korean language understanding process Finding all possible morphological structure of a word (or Eojeol) Morphological Analysis Disambiguate morphological ambiguities POS Tagging Finding a syntactic structure of an input sentence Syntactic Analysis Finding a semantic structure without using discourse context Semantic Analysis Discourse Analysis Resolving remained ambiguity of semantic analysis with discourse context information

  14. Morphological Analysis • Problem domain • Finding out • Potential parts-of-speech for a given word (for English) • Or morphological parses for a given Eojeol (for Korean) • Morphological analyzer should produce all the grammatically possible interpretations for a given word (or Eojeol) • Example of morphological analysis in Korean • When a sentence “나는 학교에 간다(na-neun hag-gyo-e gan da)” is given, the result of morphological analysis is:

  15. Morphological Analysis • Difficulties of Korean morphological analysis • Korean is • A highly agglutinative languages • An Eojeol is composed of one or more combined morphemes • Very productive • The number of Eojeols appeared in real texts is almost infinite • A morphologically complex language • Korean words (or Eojeols) are formed through compounding and derivation • Also morphological changes are frequently observed • “날/Nal/verb”+”는/Neun/connective_ending” “나는/NaNeun” • Hard to find the boundary of an unknown word • In English, words (spacing units) which are not found in a dictionary are unknown words • In Korean, only subparts of them or themselves are unknown morphemes

  16. POS Tagging • Part of Speech (POS) tagging is • A task to assign a proper POS tag to each linguistic unit such as word (in English), or morpheme (in Korean) for a given sentence • An input of POS tagger is a result of morphological analysis, and an output is a correct sequence of morpheme-POS pairs • Hidden Markov Model (HMM) based POS Tagging • Most popular and well-performed approach • Regard POS tags of morphemes in a given sentence as hidden states and find the most probable path in a lattice

  17. Syntactic Parsing • Goal • Find out a syntactic structure with a specific grammar for a given sentence • Example of parsed sentence with the phrasal structure grammar • “누나는 예쁜 꽃을 좋아한다. (Nu-Na-Neun ye-Ppeun Kkoch-eul Coh-a-Han-Ta.)” S VP VP NP NP ADJP NP .SS. Nu-NaNC NeunJX Ye-PpeuPA nEM KkochNC eulJC Coh-a-Han-TaPV+EF

  18. Syntactic Parsing • Parsing can be defined as • A problem that maps any input sentence to an appropriate syntactic tree structure (Chung, 04) • Why is the parsing so difficult? • Because of the structural ambiguity of natural language! • Several characteristics of Korean make the parsing more difficult • Relatively free-word order, constituent ellipsis … S S VP VP NP VP 보았다 어제 유진이 쇼를 보았다 어제 유진이 쇼를 보았다

  19. Syntactic Parsing • Examples of statistical parsing with simple PCFG model • “Astronomers saw stars with ears” TreeBank P(t1) = 0.0009072 P(t2) = 0.0006804 > t2 t1

  20. Semantic Analysis • Semantic analysis is • The process whereby meaning representations are composed and assigned to a user’s utterance How can I go to Korea University? What does it mean?

  21. Semantic Analysis • Example of semantic analysis

  22. Semantic Analysis • Shallow semantic analysis for a dialog system • Under the practical dialog hypothesis, we can simplify the semantic analyzing process: • Restricting domain of a dialog system reduce the ambiguity • In the pay-bill domain, the word `bank’ may not be used as the meaning of a dike • Also, if we restrict a task of a dialog system, simple methods such as concept-spotting can be enough to capture user’s intention “6시에 MBC에서 뭐 하니?” Analyzed by a concept spotting method Question Focus: Program Channel: MBC Begin_time: 18:00

  23. Discourse Analysis • Reference resolution • The omitted words (or phrases) and the pronominal references are complemented by the use of common sense and discourse information • Speech Act Identification • Speech Act: The communicative intention represented by each utterance • A dialog system should have the ability to • identify other participants’ speech act, predict next possible speech acts, and generate own utterance suitable for the speech act U: I would like to open a fixed deposit account. S: For what amount? U: Make it for 8000 dollars. Statement ­ non ­ opinion: I'm a customer since November. Statement ­ opinion: I think it's great.

  24. How to manage dialog between user and system? 1. Overview of dialog management 2. FST based Approach 3. Frame based Approach 4. Other Approaches

  25. Dialog Management • Dialog management model (or component) • Controlling the flow of the dialogbetween the system and the user, including the coordination of other components of the system • Dialog management model must solve two problems: • Keep track of the overall interaction with steady progress towards task completion • The system must have some idea of the task completion ratio • More importantly, the system must have some idea of what is yet to be done, • Robustly handle deviations from the nominal progression towards problem solution

  26. Dialog Management • One of core issues of dialog management • System-initiative: • system always has control, user only responds to system questions • User-initiative: • user always has control, system passively answers user questions • Mixed-initiative: • control switches between system and user • Classification of dialog management strategies (Allen et al. 01) • Finite state (or graph)-based strategy • Frame-based strategy • Plan-based strategy • Agent-based strategy

  27. Dialog Management • Finite-state based dialog control • Simplest dialog control method • Usually, system-initiative • Dialogue consists of a sequence of predetermined steps or states • The dialog flow is specified as a set of dialogue states with transitions denoting various alternative paths through the dialog graph • Most commercially available spoken dialog system use this form of dialogue management strategy • Example task: Long distance dialing by voice, Tele-banking system • Does not require sophisticated NLP techniques, but works only for simple tasks

  28. Dialog Management Example of finite-state based dialog management: “Pay a bill”

  29. Dialog Management • Example illustrating some limitations of finite-state based dialog system The over-informative answer cannot be accepted I already answered for that question!

  30. Dialog Management • Frame-based dialog management • More flexible approach • Mixed Initiative using fixed rules • Dialog management problem is regarded as form filing : • The form specifies all relevant information (slots) for an action • Dialog management consist of • Monitoring the form for completion • Extract relevant elements from user utterance • Asking question to user using empty slots as a trigger

  31. Dialog Management • Example of Frame-based Dialogue Control Frame:Send a message Importance of Slots Filling a slot by a user response Triggering a system response by an empty slot

  32. Dialog Management • More complex dialog management approaches • Plan (Task) Based Model: The dialogue involves interactively constructing a plan (e.g. kitchen design consultant). • Agent Based Model: Involves planning and also executing and monitoring operations in a dynamically changing world (e.g. emergency rescue coordination). • Generally require deep semantic analysis for user utterances, rich knowledge resources, and elaborate inference/reasoning methods

  33. Dialog Management • Summary of dialog management approaches

  34. Conclusion • Conversational NL interface are showing promise as a new modality for HCI, because • Natural language is most familiar way of communication in human-human interaction • It also can provide “effortless and effective” way of communication in a human-computer interaction • However, there are still serious obstacles to be overcome • Improving performances of NLP analysis components such as POS tagging, parsing, so on • Ensure domain portability of a dialog interface-based system • …

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