Search Results for author:
Found 9 papers, 4 papers with code
Scaling the Vocabulary of Non-autoregressive Models for Efficient Generative Retrieval
While standard NAR models alleviate latency and cost concerns, they exhibit a significant drop in retrieval performance (compared to AR models) due to their inability to capture dependencies between target tokens.
Unified Generative & Dense Retrieval for Query Rewriting in Sponsored Search
To leverage the strengths of both methods, we propose CLOVER-Unity, a novel approach that unifies generative and dense retrieval methods in one single model.
Active Evaluation: Efficient NLG Evaluation with Few Pairwise Comparisons
In this work, we introduce Active Evaluation, a framework to efficiently identify the top-ranked system by actively choosing system pairs for comparison using dueling bandit algorithms.
Diversity driven Query Rewriting in Search Advertising
For head and torso search queries, sponsored search engines use a huge repository of same intent queries and keywords, mined ahead of time.
Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining
However, no such data is publicly available, and hence existing models are usually trained using a single relevant response and multiple randomly selected responses from other contexts (random negatives).
A Survey of Evaluation Metrics Used for NLG Systems
The expanding number of NLG models and the shortcomings of the current metrics has led to a rapid surge in the number of evaluation metrics proposed since 2014.
Towards Transparent and Explainable Attention Models
To make attention mechanisms more faithful and plausible, we propose a modified LSTM cell with a diversity-driven training objective that ensures that the hidden representations learned at different time steps are diverse.
Let's Ask Again: Refine Network for Automatic Question Generation
It is desired that the generated question should be (i) grammatically correct (ii) answerable from the passage and (iii) specific to the given answer.
