SIGIR20_QINM

An essential task of information retrieval (IR) is to compute the probability of relevance of a document given a query. If we regard a query term or n-gram fragment as a relevance matching unit, most retrieval models firstly calculate the relevance evidence between the given query and the candidate document separately, and then accumulate these evidences as the final document relevance prediction. This kind of approach obeys the the classical probability, which is not fully consistent with human cognitive rules in the actual retrieval process, due to the possible existence of interference effect between relevance matching units. In our work, we propose a Quantum Interference inspired Neural Matching model (QINM), which can apply the interference effects to guide the construction of additional evidence generated by the interaction between matching units in the retrieval process. Experimental results on two benchmark collections demonstrate that our approach outperforms the quantum-inspired retrieval models, and some well-known neural retrieval models in the ad-hoc retrieval task.

This is the code of the paper: A Quantum Interference Inspired Neural Matching Model for Ad-hoc Retrieval

DEPENDENCIES

• python 3.5+
• numpy
• tensorflow 1.2+
• scikit-learn (sklearn)
• pandas

RUN

You can run this model by:

python train.py

The Model Architecture

REFERENCES

If you find this code is useful, please consider citing our work.

@inproceedings{jiang2020quantum,
  author = {Jiang, Yongyu and Zhang, Peng and Gao, Hui and Song, Dawei},
  title = {A Quantum Interference Inspired Neural Matching Model for Ad-Hoc Retrieval},
  year = {2020},
  isbn = {9781450380164},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3397271.3401070},
  doi = {10.1145/3397271.3401070},
  booktitle = {Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages = {19–28},
  numpages = {10},
  keywords = {information retrieval, learning-to-rank, neural matching models, quantum interference},
  location = {Virtual Event, China},
  series = {SIGIR '20}
}