ECIR2021.SQE-GAN: A Supervised Query Expansion Scheme via GAN

**Why did I choose this paper?** Because this paper uses GAN for the task of query expansion which is one of the IR tasks that is strongly related to my research.

### Main problem:
The main problem of this paper is to find a solution to make the existing methods for query expansion faster and more accurate. Query Expansion (QE) is defined as adding new terms to an input query by a user to make it more precise in order to fulfill the users' needs.
### Existing work:
Existing works on the QE can be divided into two categories:
- **Unsupervised Query Expansion** (UQE)
- **Example**: Many classical algorithms
- Probability models
- Relevance-based language models
- **Disadvantage**: noisy or even harmful
- **Supervised Query Expansion** (SQE)
- **Example**: state-of-the-art in the QE literature
- random walk
- term dependency-based approach
- boosting approach
- Learning-based approaches
- **Disadvantage**: higher response time (because of the feature extraction phase)

### Inputs:

- A set of **original queries** {q1, ..., qn},
- A set of **expanded terms** {t1, ..., tM }

### Outputs:

- top k relevant terms to query q from the candidate ones.

### Method:
**Idea:**
- **Response time**: word embedding can be used to avoid the feature extraction phase (time-consuming part) in SQE
- **Performance**:Deep learning can be used to encode the correlation between an arbitrary pair of query and expanded term

**Steps:**
- Using UQE to get expanded terms
- word embedding technique is used to transform the terms into vectors
- Using GAN, both generative and discriminative models iteratively optimize each other
- Generator re-ranks the expanded terms
- Discriminator calculates the score for the new ranking

### Experimental Setup:
**Dataset**:
- TREC Robust 2004
- 528,000 high-quality documents
- 250 queries for experiments

**Preprocessing**:

- **Stemming**: (Porter)
- **Stopwords removal**: (standard InQuery)
- **Word embedding**: Word2Vec’s Continuous Bag-of-Words (CBOW) (d=100)
- **Initial expanded terms**: 100 terms generated by UQE
- **Final selected terms**: 20 terms (of 100 initial terms)
- **40%** train, **10%** validate, **50%** test
- **Basic retrieval model**: TFIDF

**Metrics**:

- MAP (Mean Average Precision)
- Precision@k (k = [5, 10])
- NDCG

### Baselines:
- **state-of-the-art SQE scheme, SQE-TFS** response time
- **traditional UQE, KL divergence** E2
- **UQE** retrieval effect
- **RankSVM** retrieval effect
- **Deep NN** retrieval effect
- **Sequence to sequence learning** retrieval effect
- **BiLSTM**
- **Query-to-Term Attention**

### Results:
The main contribution of this paper is to propose a fast and high-performance model for QE problem. Results show that the major contribution is in the response time (37% improvement) by removing the feature extraction phase and adding a word-embedding module instead. In addition, SQL-GAN also improves the result compared with the latest deep learning-based QE solutions.
### Code:
The code of this paper is unavailable.
### Presentation:
There is no available presentation for this paper.