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Found 9 papers, 1 papers with code
A STEP towards Interpretable Multi-Hop Reasoning:Bridge Phrase Identification and Query Expansion
Through an evaluation on HotpotQA, a popular dataset for multi-hop QA, we show that our method yields: (a) improved evidence retrieval, (b) improved QA performance when using the retrieved sentences; and (c) effective and faithful explanations when answers are provided.
Divide & Conquer for Entailment-aware Multi-hop Evidence Retrieval
However, semantic equivalence is not the only relevance signal that needs to be considered when retrieving evidences for multi-hop questions.
Perturbation-based Active Learning for Question Answering
Building a question answering (QA) model with less annotation costs can be achieved by utilizing active learning (AL) training strategy.
Self-supervised Learning for Semi-supervised Temporal Language Grounding
Given a text description, Temporal Language Grounding (TLG) aims to localize temporal boundaries of the segments that contain the specified semantics in an untrimmed video.
Learning Models for Suicide Prediction from Social Media Posts
We propose a deep learning architecture and test three other machine learning models to automatically detect individuals that will attempt suicide within (1) 30 days and (2) six months, using their social media post data provided in the CLPsych 2021 shared task.
Personalized Early Stage Alzheimer's Disease Detection: A Case Study of President Reagan's Speeches
In this paper, we show that machine learning-based unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer`s disease.
Eidos, INDRA, \& Delphi: From Free Text to Executable Causal Models
Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process.
Semi-Supervised Teacher-Student Architecture for Relation Extraction
Generating a large amount of training data for information extraction (IE) is either costly (if annotations are created manually), or runs the risk of introducing noisy instances (if distant supervision is used).
Scientific Discovery as Link Prediction in Influence and Citation Graphs
We introduce a machine learning approach for the identification of {``}white spaces{''} in scientific knowledge.
