Semanlink - NLP conference AND Chatbots

[2108.13934] Robust Retrieval Augmented Generation for Zero-shot Slot Filling

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2022-01-19 About

Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge (ACL Anthology 2020)

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Aim to leverage both contextual representation of input text (deep LMs)  and  knowledge derived
from curated KBs ([Wordnet](tag:wordnet)) to improve [slot tagging](tag:slot_tagging) in the presence of [out-of-vocab](tag:oov) words ([few-shot scenario](tag:few_shot_learning))

Method:

1. retrieve potentially relevant KB entities and
encode them into distributed representations that
describe global graph-structured information
2. BERT encoder
layer to capture context-aware representations of
the sequence and attend to the KB embeddings
using multi-level graph attention
3. integrate
BERT embeddings and the KB embeddings
to predict the slot type

Contributions:

1. feasibility of applying lexical ontology
to facilitate recognizing OOV words. First to consider the large-scale background
knowledge for enhancing context-aware
slot tagging models.
2. a knowledge integration mechanism that uses multi-level graph
attention to model explicit lexical relations.
3.experiments on two benchmark datasets

> our method makes a notable difference in a
scenario where samples are linguistically diverse,
and large vocab exists.

(Better improvements when using RNN than BERT, because BERT already contains a lot of background knowledge)

2020-07-04 About

Deep Chit-Chat: deep learning for chatbots (EMNLP 2018 Tutorial)

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by Dr Wei Wu (Microsoft Xiaolce - chatbot with 200 millions users in China) and Dr Rui Yan (Peking Univ)

- 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