Semanlink - NLP event AND Slides

- Chit-chat (casual, non goal oriented) open-domain. Must be relevant to the context and diverse (informative) to be engaging.
- why creating a chat? to prove an AI can speak like a human, commercial reasons, link to services.

Task oriented vs non task oriented: this tutorial is about the second one.

Retrieval based vs generation based.

Basic knowledge of DL for chatbots:

- word embeddings
- sentence embeddings (CNN, RNN)
- dialogue modeling: seq-to-seq with attention

Response selection for retrieval based chatbots:

- single turn response selection (slides 37-57)
    - framework 1: matching with seq embeddings
    - framework 2: matching with message-response interaction (46)
    - extension of 1: KnowledgeMatching with External Knowledge (53)
    - extension of 2: RepresentationsMatching with Multiple Levels of Representations (54)
    - insights from comparison between 1 and 2 (57)
- multi turn response selection (62)
    - context is now: mess + history
    - again, 2 frameworks

Emerging directions (79):

- matching with better representations
    - Self-Attention (82)
    - fusing multiple types of repr. But how to fuse matters (83)
    - pre-training

Learning a matching model for response selection (84)

Generation based models for chatbots:

- single turn generarion (89)
    - Basic generation model
        - seq2seq
        - Attention
        - Bi-directional modeling
- multi turn generation
    - Contexts are important
    - Context sensitive models
    - Hierarchical context modeling
    - Latent variable modeling
    - Hierarchical memory networks

Diversity in conversations (99)

Content introducing (106)

Additional elements (113)

- Topics in cnversation
- Emotions

Persona in chat:

- Persona
- ...
- Knowledge
- Common sense

RL and Adversarial learning in conversations (125)

Evaluation (132)

Future trends:

- Reasoning in dialogues
- X-grounded dialogues

2018-11-06 About

Joint Models in NLP - Slides - Tutorial (EMNLP 2018) - Yue Zhang

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**Joint models: solve 2 tasks at once.**

Related tasks: POS tagging, NER, chuncking. Pipeline tasks

Motivations:

- reduce error propagation
- information exchange between tasks

Challenges:

- Joint learning
- Search

History: statistical models. 2 kinds:

- Graph-Based Methods
    - Traditional solution:
        - Score each candidate, select the highest-scored output
        - Search-space typically exponential
- Transition-Based Methods
    - Transition-Based systems: Automata
        - State: partial result during decoding, Action: operations that can be applied for state transition
        - Output constructed incrementally

- Deep learning based model
    - Neural transition based models
    - Neural graph-based models
        - Cross task
            - Seminal work: Collobert, Ronan, et al. "Natural language processing (almost) from scratch."
            - Not all tasks are mutually beneficial
            - Ramachandran, et al.  “Unsupervised pretraining for sequence to sequence learning.”
            - Peters, Matthew E., et al. "Deep contextualized word representations." (ELMo)
            - "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding."
            - ULMFIT
            - Correlation between multi-task learning and pretraining
        - Cross lingual
        - Cross domain
        - Cross standard

2018-11-06 About

Writing code for Natural language processing Research

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2018-11-05 About