Traffic incident duration prediction via a deep learning framework for text description encoding
Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported...
BERTTune: Fine-Tuning Neural Machine Translation with BERTScore
Neural machine translation models are often biased toward the limited translation references seen during training. To amend this form of overfitting, in this paper we propose fine-tuning the...
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization
Multi-document summarization (MDS) has made significant progress in recent years, in part facilitated by the availability of new, dedicated datasets and capacious language models. However, a...
RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation
To date, most abstractive summarisation models have relied on variants of the negative log-likelihood (NLL) as their training objective. In some cases, reinforcement learning has been added to...
Learning Neural Textual Representations for Citation Recommendation
With the rapid growth of the scientific literature, manually selecting appropriate citations for a paper is becoming increasingly challenging and time-consuming. While several approaches for...
Leveraging Discourse Rewards for Document-Level Neural Machine Translation
Document-level machine translation focuses on the translation of entire documents from a source to a target language. It is widely regarded as a challenging task since the translation of the...
Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition
Background. Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional...
Bidirectional LSTM-CRF for Clinical Concept Extraction
Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for...
ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems
Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new...
Bidirectional LSTM-CRF for Clinical Concept Extraction
Extraction of concepts present in patient clinical records is an essential step in clinical research. The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for clinical records...
An Investigation of Recurrent Neural Architectures for Drug Name Recognition
Drug name recognition (DNR) is an essential step in the Pharmacovigilance (PV) pipeline. DNR aims to find drug name mentions in unstructured biomedical texts and classify them into predefined...
A Shared Attention Mechanism for Interpretation of Neural Automatic Post-Editing Systems
Automatic post-editing (APE) systems aim to correct the systematic errors made by machine translators. In this paper, we propose a neural APE system that encodes the source (src) and machine...
Action recognition in still images by latent superpixel classification
Action recognition from still images is an important task of computer vision applications such as image annotation, robotic navigation, video surveillance and several others. Existing approaches...
An Adaptive Online HDP-HMM for Segmentation and Classification of Sequential Data
In the recent years, the desire and need to understand sequential data has been increasing, with particular interest in sequential contexts such as patient monitoring, understanding daily...
A non-parametric conditional factor regression model for high-dimensional input and response
In this paper, we propose a non-parametric conditional factor regression (NCFR)model for domains with high-dimensional input and response. NCFR enhances linear regression in two ways: a)...
FederatedBytes: Tokenizer-Free, Byte-Level Small Language Models for Efficient Multimodal Federated Learning
Federated Learning (FL) is a powerful machine learning framework that allows building a global model from the contributions of multiple, distributed clients, while ensuring the privacy of their...
Diversity Combining for Multi-Path LLM Reasoning
Multi-path reasoning methods such as self-consistency (SC) sample $K$ reasoning paths and choose the most frequent answer. However, their gains quickly plateau as $K$ increases, and existing...