Natural Language Processing with Less Data and More Structures

Recently, natural language processing (NLP) has had increasing success and produced extensive industrial applications. Despite being sufficient to enable these applications, current NLP systems often ignore the structures of language and heavily rely on massive labeled data. In this talk, we take a closer look at the interplay between language structures and computational methods via two lines of work. The first one studies how to incorporate linguistically-informed relations between different training data to help both text classification and sequence labeling tasks when annotated data is limited. The second part demonstrates how various structures in conversations can be utilized to generate better dialog summaries for everyday interaction.

Transcript

1. Natural Language Processing
   with Less Data and More Structures
   Diyi Yang
   School of Interactive Computing
   Georgia Tech
   

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2. NLP in the Age of Data
   ✓ Internet search
   ✓ Machine translation
   ✓ Automated assistants
   ✓ Question answering
   ✓ Sentiment analysis
   2
   

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3. Done Solving NLP ?
   3
   Complex and subtle language behavior
   ○ Social and interpersonal content in language
   Low-resourced scenarios
   ○ Real world contexts often have limited labeled data
   Structured knowledge from social interaction
   ○ Social intelligence goes beyond any fixed corpus (Bisk et al., 2020)
   ○ How to mine structured data from interactions (Sap et al., 2019)
   

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4. Built upon Systemic Functional Linguistics (Michael Halliday, 1961) and Gricean Maxims
   Seven Factors for Social NLP by Hovy and Yang, 2021, NAACL
   Social Support Exchange
   Yang et al., 2019b, SIGCHI best paper honorable mention
   Loanword and Borrowing
   Stewart et al., 2021, Society of Computation in Linguistics
   Social Role Identification
   Yang et al., 2019a, SIGCHI, best paper honorable mention
   Yang et al., 2016, ICWSM, best paper honorable mention
   Persuasion
   Yang et al., 2019, NAACL; Chen and Yang, AAAI 2021
   Humor Recognition
   Yang et al., 2015 EMNLP
   Personalized Text Generation
   Wu et al., 2021 NAACL
   4
   

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5. 5
   “Speak to our head of sales - he has over 15 years’ experience”
   “In high demand - only 2 left on our site”
   “The picture of widow Bunisia holding her baby in front of her meager
   home brings tears to my eyes.”
   ✓ Translate theories into measurable language cues, such as scarcity,
   authority, emotion, reciprocity, etc
   ✓ Model persuasion via semi-supervised nets
   ✓ Ordering of rhetorical persuasion strategies on request success
   What makes language persuasive
   (NAACL 2019, EMNLP 2020; AAAI 2021)
   

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6. Done Solving NLP ?
   6
   Complex and subtle language behavior
   ○ Social and interpersonal content in language
   Low-resourced scenarios
   ○ Real world contexts often have limited labeled data
   Structured knowledge from social interaction
   ○ Social intelligence goes beyond any fixed corpus (Bisk et al., 2020)
   ○ How to mine structured data from interactions (Sap et al., 2019)
   

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7. Overview of This Talk
   7
   ❏ Low-Resourced Scenarios
   ❏ Text Mixup for Semi-supervised Classification
   ❏ LADA for Named Entity Recognition
   ❏ Structured Knowledge from Conversations
   ❏ Summarization via Conversation Structures
   ❏ Summarization via Action and Discourse Graphs
   

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8. Overview of This Talk
   8
   ➢ Low-Resourced Scenarios
   ➢ Text Mixup for Semi-supervised Classification
   Jiaao Chen, Zichao Yang, Diyi Yang. MixText: Linguistically-Informed Interpolation of Hidden Space
   for Semi-Supervised Text Classification. ACL 2020
   

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9. https://swabhs.com/assets/pdf/talks/utaustin-guest-lecture-biases-and-interpretability.pdf
   

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10. Lots of (Socially) Low-Resourced Settings
    10
    ❏ Rich social information in text
    ❏ Often unlabeled in real-world settings
    ❏ How to utilize limited data for learning
    

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11. Prior Work on Semi-Supervised Text Classification
    ○ Confident predictions on unlabeled data for
    self-training (Lee, 2013; Grandvalet and Bengio, 2004; Meng et al., 2018)
    ○ Consistency training on unlabeled data
    (Miyato et al., 2019, 2017; Xie et al., 2019)
    ○ Pre-training on unlabeled data, then fine-tuning on
    labeled data (Devlin et al., 2019)
    11
    

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12. Why Is It Not Enough?
    ❏ Labeled and unlabeled data are treated separately
    ❏ Models may easily overfit on labeled data while still
    underfit on the unlabeled data
    12
    

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13. Text Mixup built on mixup in CV (Zhang et al., 2017; Berthelot et al., 2019)
    13
    ✓ performs linear interpolations in textual hidden space
    between different training sentences
    ✓ allows information to share across different sentences
    and creates infinite augmented training samples
    

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14. 14
    x: sentence 1
    x’: sentence 2
    y: label 1
    y’: label 2
    Text
    Mixup
    

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15. Encode separately
    15
    

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16. Encode separately
    16
    Linear interpolation
    

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17. Encode separately
    17
    Linear interpolation
    Forward-passing
    

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18. Encode separately
    18
    Linear interpolation
    Forward-passing
    Interpolate labels
    

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19. Text Mixup: Which layers to mix?
    Multi-layer encoders (e.g., BERT) capture different types of
    information in different layers (Jawahar et al., 2019)
    ● Surface, e.g., sentence length (3, 4)
    ● Syntactic, e.g., word order (6, 7)
    ● Semantic, e.g., tense, subject (7, 9, 12)
    19
    

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20. MixText = Text Mixup + Consistency Training
    for Semi-supervised Text Classification
    20
    Text
    mixup
    

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21. 21
    Back-translations
    German & Russian as intermediate language
    

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24. 24
    Interpolate labeled/unlabeled text
    Text
    mixup
    

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25. 25
    Text
    mixup
    

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26. Dataset and Baselines
    Baselines:
    ● BERT (Devlin et al., 2019)
    ● UDA (Xie et al., 2019)
    26
    

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28. Main Results
    28
    

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29. Main Results
    29
    

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30. Main Results
    30
    

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31. Ablation on Different Layer Set in Text Mixup
    Performance on AG News
    Here, 10 labeled data per class, consistent for other settings on different datasets
    31
    

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32. Learning with Limited Data
    ✓ Text Mixup performs interpolations in hidden space to
    create augmented data
    ✓ MixText ( = Text Mixup + Consistency training) works for
    text classification with limited training data
    32
    github.com/GT-SALT/MixText
    

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33. Overview of This Talk
    33
    ➢ Low-resourced scenarios
    ✓ Text Mixup for Semi-supervised Classification
    ➢ LADA for Named Entity Recognition
    Local Additivity Based Data Augmentation for Semi-supervised NER. Jiaao Chen*, Zhenghui
    Wang*, Ran Tian, Zichao Yang and Diyi Yang. EMNLP, 2020.
    

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34. Prior Work on Data Augmentation for NER
    34
    On Dec 11,2020 DATE, Pfizer-BioNTech ORG became the
    first COVID-19 DISEASE vaccine … more than 95% effective
    against the variants ... in the United Kingdom PLACE and
    South Africa PLACE.
    

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35. Prior Work on Data Augmentation for NER
    ● Adversarial attacks at token-levels (Kobayashi, 2018; Wei and Zou,
    2019; Lakshmi Narayan et al. 2019)
    ○ Suffer from creating diverse examples
    ● Paraphrasing at sentence-levels (Xie et al., 2019; Kumar et al. 2019)
    ○ Fail to maintain token-level labels
    ● Interpolation-based (Zhang et al., 2018; Miao et al., 2020; Chen et al. 2020)
    ○ Inject too much noise from random sampling
    35
    

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36. Local Additivity based Data Augmentation (LADA)
    What if directly using Mixup for NER
    36
    

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37. LADA
    37
    

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38. LADA
    38
    

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39. LADA
    39
    

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40. LADA
    40
    

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41. LADA
    41
    

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42. Local Additivity based Data Augmentation (LADA)
    What if directly using Mixup for NER?
    Didn’t work
    Strategic LADA to help:
    Intra-LADA and Inter-LADA
    42
    

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43. Intra-LADA
    ● Interpolate each token’s hidden representation with
    other tokens from the
    43
    same sentence
    Random Permutations
    

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44. Inter-LADA
    ● Interpolate each token’s hidden representation with
    each token from
    random sampling
    k-nearest neighbors
    44
    other sentences
    

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45. Inter-LADA
    45
    Israel plays down fears of war with Syria.
    Sampled Neighbours:
    1. Parliament Speaker Berri: Israel is preparing for war against Syria
    and Lebanon.
    2. Fears of an Israeli operation causes the redistribution of Syrian
    troops locations in Lebanon.
    

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46. Semi-supervised LADA = LADA + Consistency Training
    46
    Paraphrases
    Unlabeled Sentence
    Consistency Training
    

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47. Semi-supervised LADA: Consistency Training
    47
    

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48. Semi-supervised LADA: Consistency Training
    48
    and should have the same number of entities
    for any given entity type
    

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49. Datasets and Baselines
    49
    Baselines (pre-trained models)
    ● Flair (Akbik et al., 2019): BiLSTM-CRF model with pre-trained Flair embeddings
    ● BERT (Devlin et al., 2019): BERT-base-multilingual-cased
    Dataset CoNLL GermEval
    Train 14987 24000
    Dev 3466 2200
    Test 3684 5100
    Entity Types 4 12
    

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50. Results
    50
    

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51. Results
    51
    

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52. Results
    52
    

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53. Takeaways
    ● LADA performs interpolations in hidden space among
    close examples to generate augmented data
    ● The sampling strategies of mixup for sequence
    learning are important
    ● Semi-LADA designed for NER improves performances
    with limited training data
    53
    https://github.com/GT-SALT/LADA
    

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54. Overview of This Talk
    54
    ✓ Low-resourced scenarios
    ✓ Text Mixup for Semi-supervised Classification
    ✓ LADA for Named Entity Recognition
    ➢ Structured knowledge from social interaction
    ➢ Summarization via Conversation Structures
    Jiaao Chen, Diyi Yang. Multi-View Sequence-to-Sequence Models with Conversational Structure for
    Abstractive Dialogue Summarization. EMNLP 2020
    

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55. 55
    Hannah needs Betty’s
    number but Amanda does
    not have it. She needs to
    contact Larry.
    James: Hey! I have been thinking about you : )
    Hannah: Oh, that’s nice ; )
    James: What are you up to?
    Hannah: I’m about to sleep
    James: I miss u. I was hoping to see you
    Hannah: Have to get up early for work tomorrow
    James: What about tomorrow?
    Hannah: To be honest I have plans for tomorrow evening
    James: Oh ok. What about Sat then?
    Hannah: Yeah. Sure I am available on Sat
    James: I’ll pick you up at 8?
    Hannah: Sounds good. See you then
    

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56. Compared to Documents, Conversations:
    ○ Informal
    ○ Verbose
    ○ Repetitive
    ○ Reconfirmation
    ○ Hesitations
    ○ Interruptions
    56
    

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57. Classical Views for Conversations
    1. Global view treats conversation as a whole
    2. Discrete view treats it as multiple utterances
    57
    

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58. More Views from Conversation Structures
    ❏ Topic View
    One single conversation may cover multiple topics
    greetings → invitation → party details → rejection
    58
    

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59. More Views from Conversation Structures
    ❏ Topic View
    One single conversation may cover multiple topics
    greetings → invitation → party details → rejection
    ❏ Stage View
    Conversations develop certain patterns
    introduction → state problem→ solution → wrap up
    59
    

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60. Extracting Conversation Structures
    Utterance 1
    Utterance 2
    ...
    Utterance n
    Utterance 3
    SentBert
    Representation 1
    Representation 2
    ...
    Representation n
    Representation 3
    60
    

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61. Extracting Topic View
    Utterance 1
    Utterance 2
    ...
    Utterance n
    Utterance 3
    SentBert
    Representation 1
    Representation 2
    ...
    Representation n
    Representation 3
    61
    Topic 1
    Topic 2
    Topic k
    ...
    Topic 2
    C99
    

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62. Extracting Stage View
    Utterance 1
    Utterance 2
    ...
    Utterance n
    Utterance 3
    SentBert
    Representation 1
    Representation 2
    ...
    Representation n
    Representation 3
    62
    Stage 1
    Stage 1
    Stage k
    ...
    Stage 2
    HMM
    

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63. Conversation
    James: Hey! I have been thinking about you : )
    Hannah: Oh, that’s nice ; )
    James: What are you up to?
    Hannah: I’m about to sleep
    James: I miss u. I was hoping to see you
    Hannah: Have to get up early for work tomorrow
    James: What about tomorrow?
    Hannah: To be honest I have plans for tomorrow evening
    James: Oh ok. What about Sat then?
    Hannah: Yeah. Sure I am available on Sat
    James: I’ll pick you up at 8?
    Hannah: Sounds good. See you then
    

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64. Conversation Topic View
    James: Hey! I have been thinking about you : )
    Greetings
    Hannah: Oh, that’s nice ; )
    James: What are you up to?
    Today’s plan
    Hannah: I’m about to sleep
    James: I miss u. I was hoping to see you
    Hannah: Have to get up early for work tomorrow
    Plan for tomorrow
    James: What about tomorrow?
    Hannah: To be honest I have plans for tomorrow evening
    James: Oh ok. What about Sat then?
    Plan for Saturday
    Hannah: Yeah. Sure I am available on Sat
    James: I’ll pick you up at 8?
    Pick up time
    Hannah: Sounds good. See you then
    

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65. Conversation Topic View Stage View
    James: Hey! I have been thinking about you : )
    Greetings
    Openings
    Hannah: Oh, that’s nice ; )
    James: What are you up to?
    Today’s plan
    Hannah: I’m about to sleep
    Intentions
    James: I miss u. I was hoping to see you
    Hannah: Have to get up early for work tomorrow
    Plan for tomorrow
    Discussion
    James: What about tomorrow?
    Hannah: To be honest I have plans for tomorrow evening
    James: Oh ok. What about Sat then?
    Plan for Saturday
    Hannah: Yeah. Sure I am available on Sat
    James: I’ll pick you up at 8?
    Pick up time
    Hannah: Sounds good. See you then Conclusion
    

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66. Multi-view Seq2Seq to Summarize Conversations
    66
    

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67. Token-Level Encoding
    67
    

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68. 68
    View-Level Encoding
    

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69. View Slide

70. View Slide

71. Dataset SAMSum (Gliwa et al., 2019)
    & Baselines
    Baselines:
    ❏ Pointer Generator(See et al., 2017), and BART Large (Lewis et al., 2019)
    71
    # Conversations # Participants # Turns Reference Length
    Train 14732 2.4 (0.83) 11.17 (6.45) 23.44 (12.72)
    Dev 818 2.39 (0.84) 10.83 (6.37) 23.42 (12.71)
    Test 819 2.36 (0.83) 11.25 (6.35) 23.12 (12.20)
    

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72. Models Views ROUGE-1 ROUGE-2 ROUGE-L
    Pointer Generator Discrete 0.401 0.153 0.366
    BART Discrete 0.481 0.245 0.451
    BART Global 0.482 0.245 0.466
    ROUGE compares the machine-generated summary to the reference summary and counts co-occurrence of 1-grams (ROUGE-1),
    2-grams (ROUGE-2), and longest common sequence (ROUGE-L).
    Baselines in Summarizing Conversations
    

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73. Models Views ROUGE-1 ROUGE-2 ROUGE-L
    Pointer Generator Discrete 0.401 0.153 0.366
    BART Discrete 0.481 0.245 0.451
    BART Global 0.482 0.245 0.466
    BART Stage 0.487 0.251 0.472
    BART Topic 0.488 0.251 0.474
    ROUGE compares the machine-generated summary to the reference summary and counts co-occurrence of 1-grams (ROUGE-1),
    2-grams (ROUGE-2), and longest common sequence (ROUGE-L).
    Conversation Structure (Single View) Helps
    

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74. Models Views ROUGE-1 ROUGE-2 ROUGE-L
    Pointer Generator Discrete 0.401 0.153 0.366
    BART Discrete 0.481 0.245 0.451
    BART Global 0.482 0.245 0.466
    BART Stage 0.487 0.251 0.472
    BART Topic 0.488 0.251 0.474
    Multi-View BART Topic + Stage 0.493 0.256 0.477
    ROUGE compares the machine-generated summary to the reference summary and counts co-occurrence of 1-grams (ROUGE-1),
    2-grams (ROUGE-2), and longest common sequence (ROUGE-L).
    Multi-View Models Perform Better
    

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75. Human annotators rate the quality of summaries [-2 , 0, 2] (Gliwa et al. 2019)
    75
    

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76. Challenges in Conversation Summarization
    1. Informal Language Use
    76
    Greg: It’s valentine’s day! 😜
    Besty: For sombody without
    partner today is kinda miserable
    ...
    

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77. 1. Informal Language Use
    2. Multiple Participants
    77
    Greg: Do you know guys anything ...
    Bob: the most important is …
    Besty: and they will completely …
    Donald: yeah, mostly gas and oil.
    ...
    Challenges in Conversation Summarization
    

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78. 1. Informal Language Use
    2. Multiple Participants
    3. Multiple Turns
    78
    Challenges in Conversation Summarization
    Greg: Hiya, I have a favour to ask.
    Greg: Can you pick up Marcel ...
    …
    (16 turns)
    

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79. 1. Informal Language Use
    2. Multiple Participants
    3. Multiple Turns
    4. Referral & Coreference
    79
    Challenges in Conversation Summarization
    Greg: Good evening Deana!
    ...
    Besty: … belong your Cathreen!
    Greg: No. She says they aren’t hers.
    ...
    Greg: Where did you find them?
    ...
    

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80. 1. Informal Language Use
    2. Multiple Participants
    3. Multiple Turns
    4. Referral & Coreference
    5. Repetition & Interruption
    80
    Challenges in Conversation Summarization
    Greg: Well, could you pick him up?
    Besty: What if I can’t?
    Greg: Besty?
    Besty: What if I can’t?
    Greg: Can’t you, really?
    Besty: I can’t. ...
    ...
    

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81. 1. Informal Language Use
    2. Multiple Participants
    3. Multiple Turns
    4. Referral & Coreference
    5. Repetition & Interruption
    6. Negation & Rhetorical
    81
    Challenges in Conversation Summarization
    Greg: I don’t think he likes me
    Besty: Why not? He likes you
    Greg: How do u know? He’s not
    Besty: He’s looking at u
    Greg: Really? U sure ...
    ...
    

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82. 1. Informal Language Use
    2. Multiple Participants
    3. Multiple Turns
    4. Referral & Coreference
    5. Repetition & Interruption
    6. Negation & Rhetorical
    7. Role & Language Change
    82
    Challenges in Conversation Summarization
    Greg: maybe we can meet on 17th?
    Besty: I won’t also be 17th
    Greg: OK, get it
    Besty: But we could meet 14th?
    Greg: I am not sure
    ...
    

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83. 1. Informal Language Use
    2. Multiple Participants
    3. Multiple Turns
    4. Referral & Coreference
    5. Repetition & Interruption
    6. Negation & Rhetorical
    7. Role & Language Change
    83
    Challenges in Conversation Summarization
    

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84. Visualizing Challenges Percentage
    Out of 100 random examples
    ROUGE-1 ROUGE-2 ROUGE-L
    Generic 24 0.613 0.384 0.579
    1. Informal language 25 0.471 0.241 0.459
    2. Multiple participants 10 0.473 0.243 0.461
    3. Multiple turns 23 0.432 0.213 0.432
    4. Referral & coreference 33 0.445 0.206 0.430
    5. Repetition & interruption 18 0.423 0.180 0.415
    6. Negations & rhetorical 20 0.458 0.227 0.431
    7. Role & language change 30 0.469 0.211 0.450
    

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85. Overview of This Talk
    85
    ✓ Low-Resourced Scenarios
    ✓ Text Mixup for Semi-supervised Classification
    ✓ LADA for Named Entity Recognition
    ✓ Structured Knowledge from Conversations
    ✓ Summarization via Conversation Structures
    ➢ Summarization via Action and Discourse Graphs
    Jiaao Chen, Diyi Yang. Structure-Aware Abstractive Conversation Summarization via Discourse
    and Action Graphs. NAACL 2021
    

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86. Structure in Conversations: Discourse Relations
    86
    

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87. Discourse Relation Graph Extraction
    ● Pre-train a parser on an
    annotated corpus (Asher et al. 2016)
    with
    77.5 F1
    ● Predict discourse edges
    between utterances
    87
    

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88. Structure in Conversations: Action Graphs
    88
    

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89. Action Graph Extraction
    ● Transform first-person
    point-of-view to third-person
    ● Utilize OpenIE (Angeli et al., 2015)
    to extract
    “WHO-DOING-WHAT” triplets
    ● Construct the action graph
    89
    

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90. Structure-Aware Model
    90
    

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91. Utterance Encoder: BART encoder
    91
    

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92. Discourse Graph Encoder: GAT
    92
    

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93. Action Graph Encoder: GAT
    93
    

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94. Multi-granularity Decoder
    94
    

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95. Multi-granularity Decoder
    95
    ReZero
    

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96. Datasets and Baselines
    Base Models: BART-base(Lewis et al., 2019)
    96
    # Dialogues # Participants # Turns
    # Discourse
    Edges
    # Action
    Triples
    SAMSum
    Train 14732 2.40 11.17 8.47 6.72
    Dev 818 2.39 10.83 8.34 6.48
    Test 819 2.36 11.25 8.63 6.81
    ADSC Full 45 2.00 7.51 6.51 37.20
    

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97. Experiments Results (in-domain)
    Models ROUGE-1 ROUGE-2 ROUGE-L
    Pointer Generator 0.401 0.153 0.366
    BART 0.481 0.245 0.451
    BART 0.482 0.245 0.466
    BART 0.487 0.251 0.472
    BART 0.488 0.251 0.474
    Multi-View BART 0.493 0.256 0.477
    ROUGE compares the machine-generated summary to the reference summary and counts
    co-occurrence of 1-grams, 2-grams, and longest common sequence.
    97
    

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98. Experiments Results (in-domain)
    Models ROUGE-1 ROUGE-2 ROUGE-L
    Pointer Generator 40.08 15.28 36.63
    BART-base 45.15 21.66 44.46
    Multi-view BART-base 45. 0.245 0.466
    BART 0.487 0.251 0.472
    BART 0.488 0.251 0.474
    Multi-View BART 0.493 0.256 0.477
    ROUGE compares the machine-generated summary to the reference summary and counts
    co-occurrence of 1-grams, 2-grams, and longest common sequence.
    98
    Baseline
    Results
    

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99. Experiments Results (in-domain)
    Models ROUGE-1 ROUGE-2 ROUGE-L
    Pointer Generator 40.08 15.28 36.63
    BART-base 45.15 21.66 44.46
    S-BART w. Discourse 45.89 22.50 44.83
    S-BART w. Action 45.67 22.39 44.86
    BART 0.488 0.251 0.474
    Multi-View BART 0.493 0.256 0.477
    ROUGE compares the machine-generated summary to the reference summary and counts
    co-occurrence of 1-grams, 2-grams, and longest common sequence.
    99
    Baseline
    Results
    Our Model w.
    Single Graph
    

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100. Experiments Results (in-domain)
     Models ROUGE-1 ROUGE-2 ROUGE-L
     Pointer Generator 40.08 15.28 36.63
     BART-base 45.15 21.66 44.46
     S-BART w. Discourse 45.89 22.50 44.83
     S-BART w. Action 45.67 22.39 44.86
     S-BART w. Discourse & Action 46.07 22.60 45.00
     ROUGE compares the machine-generated summary to the reference summary and counts
     co-occurrence of 1-grams, 2-grams, and longest common sequence.
     100
     Baseline
     Results
     Our Model w.
     Single Graph
     Our S-BART
     

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101. Experiments Results (out-of-domain)
     ROUGE compares the machine-generated summary to the reference summary and counts
     co-occurrence of 1-grams, 2-grams, and longest common sequence.
     101
     Models ROUGE-1 ROUGE-2 ROUGE-L
     BART-base 20.90 5.04 21.23
     S-BART w. Discourse 22.42 5.58 22.16
     S-BART w. Action 30.91 20.64 35.30
     S-BART w. Discourse & Action 34.74 23.86 38.69
     

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102. Experiments Results (out-of-domain)
     ROUGE compares the machine-generated summary to the reference summary and counts
     co-occurrence of 1-grams, 2-grams, and longest common sequence.
     102
     Models ROUGE-1 ROUGE-2 ROUGE-L
     BART-base 20.90 5.04 21.23
     S-BART w. Discourse 22.42 5.58 22.16
     S-BART w. Action 30.91 20.64 35.30
     S-BART w. Discourse & Action 34.74 23.86 38.69
     

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103. Experiments Results (out-of-domain)
     ROUGE compares the machine-generated summary to the reference summary and counts
     co-occurrence of 1-grams, 2-grams, and longest common sequence.
     103
     Models ROUGE-1 ROUGE-2 ROUGE-L
     BART-base 20.90 5.04 21.23
     S-BART w. Discourse 22.42 5.58 22.16
     S-BART w. Action 30.91 20.64 35.30
     S-BART w. Discourse & Action 34.74 23.86 38.69
     

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104. Human Evaluations (Likert scale from 1 to 5)
     104
     Models Factualness Succinctness Informativenes
     s
     Ground Truth 4.29 4.40 4.06
     BART-base 3.90 4.13 3.74
     S-BART w. Discourse 4.11 4.42 3.98
     S-BART w. Action 4.17 4.29 3.95
     S-BART w. Discourse & Action 4.19 4.41 3.91
     

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105. Conclusion on Summarizing Conversations
     ✓ Conversation structures help summarizations
     ✓ Structures also improve generalization performances
     ✓ Dialogue summarizations still face MANY challenges
     105
     github.com/GT-SALT/Multi-View-Seq2Seq
     github.com/GT-SALT/Structure-Aware-BART
     

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106. Overview of This Talk
     106
     ✓ Low-Resourced Scenarios
     ✓ Text Mixup for Semi-supervised Classification
     ✓ LADA for Named Entity Recognition
     ✓ Structured Knowledge from Conversations
     ✓ Summarization via Conversation Structures
     ✓ Summarization via Action and Discourse Graphs
     

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107. Natural Language Processing
     with Less Data and More Structures
     Diyi Yang
     Twitter: @Diyi_Yang
     www.cc.gatech.edu/~dyang888
     Thank You
     

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