Search Results for author:
Found 13 papers, 3 papers with code
Rethinking E-Commerce Search
Instead of converting unstructured data (web pages, customer reviews, etc) to structured data, we instead convert structured data (product inventory, catalogs, taxonomies, etc) into textual data, which can be easily integrated into the text corpus that trains LLMs.
Mitigating Pooling Bias in E-commerce Search via False Negative Estimation
Efficient and accurate product relevance assessment is critical for user experiences and business success.
An Embedding-Based Grocery Search Model at Instacart
To train the model efficiently on noisy data, we propose a self-adversarial learning method and a cascade training method.
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications.
Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model
Recently, there is an effort to extend fine-grained entity typing by using a richer and ultra-fine set of types, and labeling noun phrases including pronouns and nominal nouns instead of just named entity mentions.
Ranked #1 on
Entity Typing
on Ontonotes v5 (English)
(using extra training data)
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning
In this work, we show that this paradigm might inherit the adversarial vulnerability of the centralized neural network, i. e., it has deteriorated performance on adversarial examples when the model is deployed.
SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension
A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS).
KBQA: Learning Question Answering over QA Corpora and Knowledge Bases
Based on these templates, our QA system KBQA effectively supports binary factoid questions, as well as complex questions which are composed of a series of binary factoid questions.
A Transfer-Learnable Natural Language Interface for Databases
In this work, we introduce a general purpose transfer-learnable NLI with the goal of learning one model that can be used as NLI for any relational database.
Verb Pattern: A Probabilistic Semantic Representation on Verbs
In this paper, we introduce verb patterns to represent verbs' semantics, such that each pattern corresponds to a single semantic of the verb.
Entity Suggestion by Example using a Conceptual Taxonomy
This paper provides a query processing method based on the relevance models between entity sets and concepts.
Data-Driven Metaphor Recognition and Explanation
Recognizing metaphors and identifying the source-target mappings is an important task as metaphorical text poses a big challenge for machine reading.
