siat-nlp/MAMS-for-ABSA

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siat-nlp/MAMS-for-ABSA

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A Multi-Aspect Multi-Sentiment Dataset for aspect-based sentiment analysis.

MAMS-for-ABSA

This repository contains the data and code for the paper "A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis", EMNLP-IJCNLP 2019, [paper].

MAMS

MAMS is a challenge dataset for aspect-based sentiment analysis (ABSA), in which each sentences contain at least two aspects with different sentiment polarities. MAMS dataset contains two versions: one for aspect-term sentiment analysis (ATSA) and one for aspect-category sentiment analysis (ACSA).

Requirements

pytorch==1.1.0
spacy==2.1.8
pytorch-pretrained-bert==0.6.2
adabound==0.0.5
pyyaml==5.1.2
numpy==1.17.2
scikit-learn==0.21.3
scipy==1.3.1

Quick Start

Put the pretrained GloVe(http://nlp.stanford.edu/data/wordvecs/glove.840B.300d.zip) file glove.840B.300d.txt in folder ./data. Modify config.py to select task, model and hyper-parameters. When mode is set to term, base_path should point to an ATSA dataset. When mode is set to category, base_path should point to an ACSA dataset.

Preprocessing

python preprocess.py

Train

python train.py

Test

python test.py

Acknowledgement

The BERT model pretrained by huggingface(https://github.com/huggingface/pytorch-transformers) is used in our experiments.

GALAXY

Official repository of the AAAI'2022 paper "GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection"

HanFei

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TTOS

Official repository of the EMNLP'2020 paper "Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training"

transformer-pytorch

A PyTorch implementation of transformer for text generation.

DDMN

Code and data for the paper "Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems".

NLP-docs

Docs of NLP/deep Learning/machine learning, etc. https://siat-nlp.github.io/docs

MGMA

Official repository of the KBS'2021 paper "Multi-Goal Multi-agent Learning for Task-oriented Dialogue with Two-to-One Teacher-Student Learning"

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IPSRec

Code and data for the SIGIR'2022 paper "Item-Provider Co-learning for Sequential Recommendation" (IPSRec)

seq2seq-pytorch

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