Code Implementation of "Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction".

Related tags

Text Data & NLPnlp
Overview

Span-ASTE: Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction

***** New March 31th, 2022: Scikit-Style API for Easy Usage *****

PWC Colab Jupyter

This repository implements our ACL 2021 research paper Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction. Our goal is to extract sentiment triplets of the format (aspect target, opinion expression and sentiment polarity), as shown in the diagram below.

Installation

Data Format

Our span-based model uses data files where the format for each line contains one input sentence and a list of output triplets:

sentence#### #### ####[triplet_0, ..., triplet_n]

Each triplet is a tuple that consists of (span_a, span_b, label). Each span is a list. If the span covers a single word, the list will contain only the word index. If the span covers multiple words, the list will contain the index of the first word and last word. For example:

It also has lots of other Korean dishes that are affordable and just as yummy .#### #### ####[([6, 7], [10], 'POS'), ([6, 7], [14], 'POS')]

For prediction, the data can contain the input sentence only, with an empty list for triplets:

sentence#### #### ####[]

Predict Using Model Weights

  • First, download and extract pre-trained weights to pretrained_dir
  • The input data file path_in and output data file path_out have the same data format.
from wrapper import SpanModel

model = SpanModel(save_dir=pretrained_dir, random_seed=0)
model.predict(path_in, path_out)

Model Training

  • Configure the model with save directory and random seed.
  • Start training based on the training and validation data which have the same data format.
model = SpanModel(save_dir=save_dir, random_seed=random_seed)
model.fit(path_train, path_dev)

Model Evaluation

  • From the trained model, predict triplets from the test sentences and output into path_pred.
  • The model includes a scoring function which will provide F1 metric scores for triplet extraction.
model.predict(path_in=path_test, path_out=path_pred)
results = model.score(path_pred, path_test)

Research Citation

If the code is useful for your research project, we appreciate if you cite the following paper:

@inproceedings{xu-etal-2021-learning,
    title = "Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction",
    author = "Xu, Lu  and
      Chia, Yew Ken  and
      Bing, Lidong",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.367",
    doi = "10.18653/v1/2021.acl-long.367",
    pages = "4755--4766",
    abstract = "Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term. Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each target word and opinion word. Thereby, they cannot perform well on targets and opinions which contain multiple words. Our proposed span-level approach explicitly considers the interaction between the whole spans of targets and opinions when predicting their sentiment relation. Thus, it can make predictions with the semantics of whole spans, ensuring better sentiment consistency. To ease the high computational cost caused by span enumeration, we propose a dual-channel span pruning strategy by incorporating supervision from the Aspect Term Extraction (ATE) and Opinion Term Extraction (OTE) tasks. This strategy not only improves computational efficiency but also distinguishes the opinion and target spans more properly. Our framework simultaneously achieves strong performance for the ASTE as well as ATE and OTE tasks. In particular, our analysis shows that our span-level approach achieves more significant improvements over the baselines on triplets with multi-word targets or opinions.",
}
Comments
  • Train model for new data collected from social media

    Train model for new data collected from social media

    Hi, I would like to train this model in a new dataset with another language "Bahasa" as aspects and opinions of them, especially in social media textual data, constitute a span of words with multiple lengths. How to execute the file accordingly?

    opened by Lafandi 7
  • command命令错误

    command命令错误

    {'command': 'cd /home/data2/yj/Span-ASTE && allennlp train outputs/14lap/seed_0/config.jsonnet --serialization-dir outputs/14lap/seed_0/weights --include-package span_model'} /bin/sh: allennlp: 未找到命令,请问这个在什么文件里改,一直没找到。。。

    opened by lzf00 6
  • Retrain with new language

    Retrain with new language

    Hi, I have some questions (sorry if this is some kind of beginners question, I am new in this field). I want to change the word embedder to the BERT that is pretrained with my language (Indonesia, using indobert). Can you give some tips on how to change the embedder to my language? Thanks!

    opened by rdyzakya 5
  • Using the notebook when there is no GPU

    Using the notebook when there is no GPU

    Hello! Thank you for sharing this work! I was wondering how I can use the demo notebook locally when there is no GPU?

    When running the cell under "# Use pretrained SpanModel weights for prediction, " I got this error:

    2022-07-06 12:28:07,840 - INFO - allennlp.common.plugins - Plugin allennlp_models available Traceback (most recent call last): File "/Users/xiaoqingwan/opt/miniconda3/envs/absa/bin/allennlp", line 8, in sys.exit(run()) File "/Users/xiaoqingwan/opt/miniconda3/envs/absa/lib/python3.7/site-packages/allennlp/main.py", line 34, in run main(prog="allennlp") File "/Users/xiaoqingwan/opt/miniconda3/envs/absa/lib/python3.7/site-packages/allennlp/commands/init.py", line 118, in main args.func(args) File "/Users/xiaoqingwan/opt/miniconda3/envs/absa/lib/python3.7/site-packages/allennlp/commands/predict.py", line 205, in _predict predictor = _get_predictor(args) File "/Users/xiaoqingwan/opt/miniconda3/envs/absa/lib/python3.7/site-packages/allennlp/commands/predict.py", line 105, in _get_predictor check_for_gpu(args.cuda_device) File "/Users/xiaoqingwan/opt/miniconda3/envs/absa/lib/python3.7/site-packages/allennlp/common/checks.py", line 131, in check_for_gpu " 'trainer.cuda_device=-1' in the json config file." + torch_gpu_error allennlp.common.checks.ConfigurationError: Experiment specified a GPU but none is available; if you want to run on CPU use the override 'trainer.cuda_device=-1' in the json config file. module 'torch.cuda' has no attribute '_check_driver'

    I changed cuda_device to -1 in the jsonnet files from your folder training_config. But still no luck.

    opened by xiaoqingwan 5
  • Suggestions to run it against other datasets

    Suggestions to run it against other datasets

    Hi! I'm pretty new to deep learning and ASTE.

    Can you please suggest to me the necessary steps to run this against another dataset? Do I need to follow this data structure (https://github.com/xuuuluuu/SemEval-Triplet-data/blob/master/README.md#data-description) on my dataset by labeling it? How can I modify the code on Colab for new datasets? thank you Any other advice?

    Thank you

    opened by Jurys22 4
  • Running problem

    Running problem

    Hello, I have a question, I want to ask you. I use Pycharm to run your project, but report an error in the main.py file, prompt: ModuleNotFoundError: No module named '_jsonnet'. I guess the main reason because import _jsonnet # noqa. Can you tell me a solution? Thank you very much. 微信图片_20211123164149

    opened by FengLingCong13 4
  • Data format

    Data format

    Excuse me,how do you label the data to make the input format be as follows:

    Exactly as posted plus a great value .####Exactly=O as=O posted=O plus=O a=O great=O value=T-POS .=O####Exactly=O as=O posted=O plus=O a=O great=S value=O .=O####[([6], [5], 'POS')] The specs are pretty good too .####The=O specs=T-POS are=O pretty=O good=O too=O .=O####The=O specs=O are=O pretty=O good=S too=O .=O####[([1], [4], 'POS')]

    opened by arroyoaaa 4
  • Interpretation of the results

    Interpretation of the results

    Hello, I was looking at the file in

    /content/Span-ASTE/model_outputs/aste_sample_c7b00b66bf7ec669d23b80879fda043d/predict_dev.jsonl

    I would like to know what are the numbers in the predicted_ner and predicted_relations such as:

    [[0, 0, 1, 1, 'NEG', 2.777, 0.971]]

    What are 2.777 and 0.971 referring to?

    Thank you

    opened by Jurys22 3
  •   I installed the package according to the requirements. I wanted to use the pre trained model to make predictions, but it failed to run.

    I installed the package according to the requirements. I wanted to use the pre trained model to make predictions, but it failed to run.

    I installed the package according to the requirements. I wanted to use the pre trained model to make predictions, but it failed to run. Two error was reported: 1. allennlp.common.checks.ConfigurationError: Extra parameters passed to SpanModel: {'relation_head_type': 'proper', 'use_bilstm_after_embedder': False, 'use_double_mix_embedder': False, 'use_ner_embeds': False} Traceback (most recent call last): File "X:\workspace\python\[email protected]\Span-ASTE\aste\test.py", line 4, in model.predict('test.txt', "pred.txt") File "X:\workspace\python\[email protected]\Span-ASTE\aste\wrapper.py", line 83, in predict with open(path_temp_out) as f: 2. FileNotFoundError: [Errno 2] No such file or directory: 'X:\workspace\python\papercode\@aspect\Span-ASTE\pretrained_dir\temp_data\pred_out.json'

    opened by SiriusXT 2
  • IndexError: List assignment index out of range

    IndexError: List assignment index out of range

    I've annotated my own data and tried to train the model with the annotated data, and run into this error here (see below). The command runs successfully, but the model doesn't train on the annotated data, going into the out.log files we see this error. The annotated data follows the correct format as I'm able to preview it in the Data Exploration command. Any help would be appreciated please! :)

    image

    opened by jasonhuynh83 2
  • No such file or directory: 'pretrained_14res/temp_data/pred_out.json'

    No such file or directory: 'pretrained_14res/temp_data/pred_out.json'

    Installed it successfully in MAC OS but getting the error pred_out.json not found. Not sure why is this working successfully in colab but not when I am installing it in my local machine. Can any one help me . I have downloaded the folder correctly. Contains all the required files. I have tried with 14lap and 14res but both have same issue. Screenshot 2022-09-22 at 7 48 09 PM

    opened by dipanmoy 2
  • python wrapper.py

    python wrapper.py

    hi ,I'm puzzled when running wrapper.py, the following appears which I can't understand NAME wrapper.py

    SYNOPSIS wrapper.py GROUP | COMMAND

    GROUPS GROUP is one of the following:

     json
       JSON (JavaScript Object Notation) <http://json.org> is a subset of JavaScript syntax (ECMA-262 3rd edition) used as a lightweight data interchange format.
    
     os
       OS routines for NT or Posix depending on what system we're on.
    
     shutil
       Utility functions for copying and archiving files and directory trees.
    
     sys
       This module provides access to some objects used or maintained by the interpreter and to functions that interact strongly with the interpreter.
    
     List
       The central part of internal API.
    
     Tuple
       Tuple type; Tuple[X, Y] is the cross-product type of X and Y.
    
     Optional
       Internal indicator of special typing constructs. See _doc instance attribute for specific docs.
    

    COMMANDS COMMAND is one of the following:

     Namespace
       Simple object for storing attributes.
    
     Path
       PurePath subclass that can make system calls.
    
     train_model
       Trains the model specified in the given [`Params`](../common/params.md#params) object, using the data and training parameters also specified in that object, and saves the results in `serialization_dir`
    
    opened by xian-xian 2
  •  ConfigurationError: key

    ConfigurationError: key "dataset_reader" is required

    I was trying to replicate the same to Azure Databricks. While I'm training to train the model, I am getting the "ConfigurationError: key "dataset_reader" is required" error. For your reference

    image image image image

    Is this solution can be implemented in the Databricks environment ? @chiayewken

    opened by tsharisaravanan 1
  • Optional: Set up NLTK packages这个是什么意思呀,可以帮忙讲解一下吗

    Optional: Set up NLTK packages这个是什么意思呀,可以帮忙讲解一下吗

    Optional: Set up NLTK packages

    if [[ -f punkt.zip ]]; then mkdir -p /home/admin/nltk_data/tokenizers cp punkt.zip /home/admin/nltk_data/tokenizers fi if [[ -f wordnet.zip ]]; then mkdir -p /home/admin/nltk_data/corpora cp wordnet.zip /home/admin/nltk_data/corpora fi 不明白这个什么意思,研一学生求求了

    opened by xian-xian 5
  • An error for Posixpath

    An error for Posixpath

    Hi, I have some questions to ask you.

    The params_file is a string type, but this error has occurred as follow:

    Traceback (most recent call last): File "/Span-ASTE-main/aste/wrapper.py", line 177, in model.fit(path_train, path_dev) File "/Span-ASTE-main/aste/wrapper.py", line 54, in fit test_data_path=str(self.save_temp_data(path_dev, "dev")), File "/lib/python3.7/site-packages/allennlp/common/params.py", line 462, in from_file file_dict = json.loads(evaluate_file(params_file, ext_vars=ext_vars)) TypeError: argument 1 must be str, not PosixPath

    By the way, what should I start your code, the "main.py" or "wrapper.py".

    opened by Chen-PengF 1
  • demo file not working, No module named 'data_utils', No module named 'data_utils'

    demo file not working, No module named 'data_utils', No module named 'data_utils'

    Hi,

    I tried to run the demo file, but it shows error of "No module named 'data_utils'". The error coming from the line "No module named 'data_utils'"

    opened by qi-xia 1
Owner
Chia Yew Ken
Hi! I'm a 2nd year PhD Student with SUTD and Alibaba. My research interests currently include zero-shot learning, structured prediction and sentiment analysis.
Chia Yew Ken
Use Tensorflow2.7.0 Build OpenAI'GPT-2

TF2_GPT-2 Use Tensorflow2.7.0 Build OpenAI'GPT-2 使用最新tensorflow2.7.0构建openai官方的GPT-2 NLP模型 优点 使用无监督技术 拥有大量词汇量 可实现续写(堪比“xx梦续写”) 实现对话后续将应用于FloatTech的Bot

Watermelon 9 Sep 13, 2022
Language-Agnostic SEntence Representations

LASER Language-Agnostic SEntence Representations LASER is a library to calculate and use multilingual sentence embeddings. NEWS 2019/11/08 CCMatrix is

Facebook Research 3.2k Jan 04, 2023
Code for Findings of ACL 2022 Paper "Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors"

SWRM Code for Findings of ACL 2022 Paper "Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors" Clone Clone th

14 Jan 03, 2023
A Survey of Natural Language Generation in Task-Oriented Dialogue System (TOD): Recent Advances and New Frontiers

A Survey of Natural Language Generation in Task-Oriented Dialogue System (TOD): Recent Advances and New Frontiers

Libo Qin 132 Nov 25, 2022
American Sign Language (ASL) to Text Converter

Signterpreter American Sign Language (ASL) to Text Converter Recommendations Although there is grayscale and gaussian blur, we recommend that you use

0 Feb 20, 2022
ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab

AliceMind AliceMind: ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab This repository provides pre-trained encode

Alibaba 1.4k Jan 04, 2023
Converts text into a PDF of handwritten notes

Text To Handwritten Notes Converts text into a PDF of handwritten notes Explore the docs » · Report Bug · Request Feature · Steps: $ git clone https:/

UVSinghK 63 Oct 09, 2022
Japanese Long-Unit-Word Tokenizer with RemBertTokenizerFast of Transformers

Japanese-LUW-Tokenizer Japanese Long-Unit-Word (国語研長単位) Tokenizer for Transformers based on 青空文庫 Basic Usage from transformers import RemBertToken

Koichi Yasuoka 3 Dec 22, 2021
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing

Introduction Funnel-Transformer is a new self-attention model that gradually compresses the sequence of hidden states to a shorter one and hence reduc

GUOKUN LAI 197 Dec 11, 2022
A sample project that exists for PyPUG's "Tutorial on Packaging and Distributing Projects"

A sample Python project A sample project that exists as an aid to the Python Packaging User Guide's Tutorial on Packaging and Distributing Projects. T

Python Packaging Authority 4.5k Dec 30, 2022
Py65 65816 - Add support for the 65C816 to py65

Add support for the 65C816 to py65 Py65 (https://github.com/mnaberez/py65) is a

4 Jan 04, 2023
Code for "Generative adversarial networks for reconstructing natural images from brain activity".

Reconstruct handwritten characters from brains using GANs Example code for the paper "Generative adversarial networks for reconstructing natural image

K. Seeliger 2 May 17, 2022
A Fast Sequence Transducer Implementation with PyTorch Bindings

transducer A Fast Sequence Transducer Implementation with PyTorch Bindings. The corresponding publication is Sequence Transduction with Recurrent Neur

Awni Hannun 184 Dec 18, 2022
Twitter bot that uses NLP models to summarize news articles referenced in a user's twitter timeline

Twitter-News-Summarizer Twitter bot that uses NLP models to summarize news articles referenced in a user's twitter timeline 1.) Extracts all tweets fr

Rohit Govindan 1 Jan 27, 2022
A simple word search made in python

Word Search Puzzle A simple word search made in python Usage $ python3 main.py -h usage: main.py [-h] [-c] [-f FILE] Generates a word s

Magoninho 16 Mar 10, 2022
A modular Karton Framework service that unpacks common packers like UPX and others using the Qiling Framework.

Unpacker Karton Service A modular Karton Framework service that unpacks common packers like UPX and others using the Qiling Framework. This project is

c3rb3ru5 45 Jan 05, 2023
Local cross-platform machine translation GUI, based on CTranslate2

DesktopTranslator Local cross-platform machine translation GUI, based on CTranslate2 Download Windows Installer You can either download a ready-made W

Yasmin Moslem 29 Jan 05, 2023
A natural language processing model for sequential sentence classification in medical abstracts.

NLP PubMed Medical Research Paper Abstract (Randomized Controlled Trial) A natural language processing model for sequential sentence classification in

Hemanth Chandran 1 Jan 17, 2022
Partially offline multi-language translator built upon Huggingface transformers.

Translate Command-line interface to translation pipelines, powered by Huggingface transformers. This tool can download translation models, and then us

Richard Jarry 8 Oct 25, 2022
Multilingual text (NLP) processing toolkit

polyglot Polyglot is a natural language pipeline that supports massive multilingual applications. Free software: GPLv3 license Documentation: http://p

RAMI ALRFOU 2.1k Jan 07, 2023