Awesome Neural Models for Semantic Match
A collection of papers maintained by MatchZoo Team.
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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.
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. 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 Indentification](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) | response | response |
pip3 install -r requirements.txt
python3 healthcheck.py