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Found 11 papers, 3 papers with code
Scalable and Effective Generative Information Retrieval
This paper represents an important milestone in generative retrieval research by showing, for the first time, that generative retrieval models can be trained to perform effectively on large-scale standard retrieval benchmarks.
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction
Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations.
Low Resource Named Entity Recognition
named-entity-recognition
+2
Mixed Attention Transformer for Leveraging Word-Level Knowledge to Neural Cross-Lingual Information Retrieval
In the monolingual retrieval task, because of the same lexical inputs, it is easier for model to identify the query terms that occurred in documents.
Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach
As content moderation is itself harmful to the people doing it, we desire to reduce the burden by improving the automatic detection of hate speech.
Cultural Vocal Bursts Intensity Prediction
Data Augmentation
+2
Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence
Automated event extraction in social science applications often requires corpus-level evaluations: for example, aggregating text predictions across metadata and unbiased estimates of recall.
A Neighbourhood Framework for Resource-Lean Content Flagging
The framework is based on a nearest-neighbour architecture.
Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go
The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self harm, and many other.
Semantic Driven Fielded Entity Retrieval
A common approach for knowledge-base entity search is to consider an entity as a document with multiple fields.
A Multi-Task Architecture on Relevance-based Neural Query Translation
We describe a multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query translation.
Named Entity Recognition with Extremely Limited Data
Traditional information retrieval treats named entity recognition as a pre-indexing corpus annotation task, allowing entity tags to be indexed and used during search.
Two-stage Cascaded Classifier for Purchase Prediction
In this paper we describe our machine learning solution for the RecSys Challenge, 2015.
