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awesome-neural-models-for-semantic-match

(★ 775)

A curated list of papers dedicated to neural text (semantic) matching.

File Explorer

Download Latest Version (.zip)

- chart.html
- table.html
- Ad-hoc-Information-Retrieval.md
- awesome.svg
- equation.svg
- not-in-plan.svg
- plan.svg
- progress.svg
- ready.svg
- Community-Question-Answering.md
- LFQA.md
- Natural-Language-Inference.md
- Paraphrase-Identification.md
- Response-Retrieval.md
.gitignore
_config.yml
Gemfile
healthcheck.py
LICENSE
README.md
requirements.txt

# Installation Guide

1. Get the code

git clone https://github.com/NTMC-Community/awesome-neural-models-for-semantic-match

Downloads the entire project code from GitHub to your computer.

cd awesome-neural-models-for-semantic-match

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip3 install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

3. Ruby

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Ruby Ruby is required to use the bundle command.

bundle install

Installs the Ruby libraries listed in the Gemfile.

✅ If bundle install finishes without errors, continue with the run command from the README (e.g. rails server).

// repository documentation

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<div align="center">
    <img width="300" src="artworks/awesome.svg" alt="Awesome">
    <br><br>
    <p><b>Awesome Neural Models for Semantic Match</b></p>
</div>
<br>
<p align="center">
    <sub>A collection of papers maintained by MatchZoo Team.</sub>
    <br>
    <sub>Checkout our open source toolkit <a href="https://github.com/NTMC-Community/MatchZoo-py">MatchZoo</a> for more information!</sub>
</p>
<br>

Text matching is a core component in many natural language processing tasks, where many task can be viewed as a matching between two texts input.

<div align="center">
    <img width="300" src="artworks/equation.svg" alt="equation">
</div>

Where **s** and **t** are source text input and target text input, respectively. The **psi** and **phi** are representation function for input **s** and **t**, respectively. The **f** is the interaction function, and **g** is the aggregation function. More detailed explaination about this formula can be found on [A Deep Look into Neural Ranking Models for Information Retrieval](https://arxiv.org/abs/1903.06902). The representative matching tasks are as follows:

|                                          **Tasks**                                           | **Source Text** |     **Target Text**      |
| :------------------------------------------------------------------------------------------: | :-------------: | :----------------------: |
| [Ad-hoc Information Retrieval](Ad-hoc-Information-Retrieval/Ad-hoc-Information-Retrieval.md) |      query      | document (title/content) |
| [Community Question Answering](Community-Question-Answering/Community-Question-Answering.md) |    question     |     question/answer      |
|     [Paraphrase Identification](Paraphrase-Identification/Paraphrase-Identification.md)     |     string1     |         string2          |
|    [Natural Language Inference](Natural-Language-Inference/Natural-Language-Inference.md)    |     premise     |        hypothesis        |
|                [Response Retrieval](Response-Retrieval/Response-Retrieval.md)                |    context/utterances     |         response         |
|                [Long Form Question Answering](LFQA/LFQA.md)                |    question+document    |         answer         |

### Healthcheck

```python
pip3 install -r requirements.txt
python3 healthcheck.py
```