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Lecture Notes
08-NLU

Natural/Spoken Language Understanding

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Definition

Natural language understanding

  • Representing the semantics of natural language
  • Possible view: Translation from natural language to representation of meaning

Difficulties

  • Ambiguities

    • Lexical

    • Syntax

    • Referential

  • Vagueness
    • E.g., “I had a late lunch.”
  • Dimensons
    • Depth: Shallow vs Deep
    • Domain: Narrow vs Open

Examples

Siri (2011)

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Dialog Modeling

Dialog system / Conversational agent

  • Computer system that converse with a human

  • Coherent structure

  • Different modalities:

    • Text, speech, graphics, haptics, gestures

Components

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Input recognition

  • Different modalities

    • Automatic speech recognition (ASR)
    • Gesture recognition

  • Transformation

    Input modality (e.g. speech) –> text

  • May introduce first errors

  • High influence on the performance of an dialog system

Natural language understanding (NLU)

  • Semantic interpretation of written text

  • Transformation from natural language to semantic representation

  • Representations:

    • Deep vs Shallow

    • Domain-dependent vs. domain independent

Dialog manager (DM)

  • Manage flow of conversation
  • Input: Semantic representation of the input
  • Output: Semantic representation of the output
  • Utilize additional knowledge

    • User information

    • Dialog History

    • Task-specific information

Natural language generation (NLG)

Generate natural language from semantic representation

  • Input: Semantic output representation of the dialog manager
  • Output: Natural language text for the user

Output rendering

Generate correct output

  • e.g. Text-to-Speech (TTS) for Spoken Dialog Systems

Natural Language understanding

Approaches

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Output representation

  • Relation instances
    • (Larry Page, founder, Google)
  • Logical forms
    • Love(Mary, John)
  • Scalar
    • Positive/Negative 0.9
  • Vector
    • Hidden representation/ Word embeddings

Algorithms

  • Rule-based / Template
  • Machine learning
    • Conditional random fields
    • Support Vector Machine
    • Neural Networks / Deep learning

Semantic Parsing

  • Parse natural language sentence into semantic representation

  • Machine learning approaches most successful 👏

  • Most common approach:

    • Shallow Semantic Parsing / Semantic Role Labeling

  • Most important resources:

PropBank

  • Proposition Bank (PropBank)

  • Labels for all sentence in the English Penn TreeBank

  • Defines semantic based on the verbs of the sentence

  • Verbs: Define different senses of the verbs

  • Sense: Number of Arguments important to this sense (Often only numbers)

    • Arg0: Proto-Agent

    • Arg1: Proto-Patient

    • Arg2: mostly benefactive, instrument, attribute, or end state

    • Arg3: start point, benefactive, instrument, or attribute

Example: “agree”
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Example: “fall”
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PropBank ArgM
  • TMP : when? yesterday evening, now
  • LOC : where? at the museum, in San Francisco
  • DIR : where to/from? down, to Bangkok
  • MNR : how? clearly, with much enthusiasm
  • PRP/CAU : why? because … , in response to the ruling
  • REC : themselves, each other
  • ADV : miscellaneous
  • PRD : secondary predication …ate the meat raw
🔴 Problem

Different words, Predicate expressed by noun

Example

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More see: SRL数据集(1): Proposition Bank 数据集介绍

FrameNet

  • Roles based on Frames
  • Frame: holistic background knowledge that unites these words
  • Frame-elements: Frame-specific semantic roles
Example 1
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Example 2
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Semantic Role labeling

  • Task: Automatically finding semantic roles for each argument of each predicate

  • Approach: Maching Learning

  • High level algorithm:

    1. Parse sentence (Syntax tree)

    2. Find Predicates

    3. For every node in tree: Decide semantic role

Spoken Language understanding

Natural language processing for spoken input

Difficulties

  • Less grammatically speech

    • Partial Sentences

    • Disfluencies (Self correction, hesitations, repetitions)

  • Robust to noise

    • ASR errors

    • Techniques:

      • Confidence
      • Multiple hypothesis

  • No Structure information

    • Punctuation

    • Text segmentation

Approach

Domain Detection

  • Motivated by Call Centers
    • Many agents with specialization on one topic (Billing inquiries, technical support requests, sales inquiries, etc.)
  • First techniques: Menus to find appropriate agent
  • Automatic task:
    • Given the utterance find the correct agent Utterance classification task
    • Utterance classification task
  • Input: Utterance
  • Output: Topic

Intention determination

  • Domain-dependent utterance classes
    • e.g. Find_Flight
  • Task: Assign class to Utterance
  • Use similar technique

Slot filling

Sequence labeling task: Assign semantic class label to every word and history

  • History: previous words and labels

Example:

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Success of deep learning in other approaches:

  • RNN-based approach
  • Find most probable label given word and history