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Found 24 papers, 15 papers with code
PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs
Research interest in task-oriented dialogs has increased as systems such as Google Assistant, Alexa and Siri have become ubiquitous in everyday life.
SUPP.AI: Finding Evidence for Supplement-Drug Interactions
We fine-tune the contextualized word representations of the RoBERTa language model using labeled DDI data, and apply the fine-tuned model to identify supplement interactions.
Structural Scaffolds for Citation Intent Classification in Scientific Publications
Identifying the intent of a citation in scientific papers (e. g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature.
Ranked #2 on
Sentence Classification
on SciCite
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing
Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift.
Combining Distant and Direct Supervision for Neural Relation Extraction
In relation extraction with distant supervision, noisy labels make it difficult to train quality models.
Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context
Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies.
Construction of the Literature Graph in Semantic Scholar
We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery.
A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
In the first task, we show that simple models can predict whether a paper is accepted with up to 21% error reduction compared to the majority baseline.
Extracting Scientific Figures with Distantly Supervised Neural Networks
Non-textual components such as charts, diagrams and tables provide key information in many scientific documents, but the lack of large labeled datasets has impeded the development of data-driven methods for scientific figure extraction.
Content-Based Citation Recommendation
We present a content-based method for recommending citations in an academic paper draft.
The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction
This paper describes our submission for the ScienceIE shared task (SemEval- 2017 Task 10) on entity and relation extraction from scientific papers.
Ontology-Aware Token Embeddings for Prepositional Phrase Attachment
Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language.
Semi-supervised sequence tagging with bidirectional language models
Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks.
Ranked #50 on
Named Entity Recognition (NER)
on CoNLL 2003 (English)
DyNet: The Dynamic Neural Network Toolkit
In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.
Massively Multilingual Word Embeddings
We introduce new methods for estimating and evaluating embeddings of words in more than fifty languages in a single shared embedding space.
Many Languages, One Parser
We train one multilingual model for dependency parsing and use it to parse sentences in several languages.
Unsupervised POS Induction with Word Embeddings
Unsupervised word embeddings have been shown to be valuable as features in supervised learning problems; however, their role in unsupervised problems has been less thoroughly explored.
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
We introduce a framework for unsupervised learning of structured predictors with overlapping, global features.
