Search Results for author:
Found 12 papers, 9 papers with code
Orb: A Fast, Scalable Neural Network Potential
We introduce Orb, a family of universal interatomic potentials for atomistic modelling of materials.
PAWLS: PDF Annotation With Labels and Structure
Adobe's Portable Document Format (PDF) is a popular way of distributing view-only documents with a rich visual markup.
PySBD: Pragmatic Sentence Boundary Disambiguation
In this paper, we present a rule-based sentence boundary disambiguation Python package that works out-of-the-box for 22 languages.
S2ORC: The Semantic Scholar Open Research Corpus
We introduce S2ORC, a large corpus of 81. 1M English-language academic papers spanning many academic disciplines.
Knowledge Enhanced Contextual Word Representations
Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities.
Ranked #9 on
Relation Classification
on TACRED
Grammar-based Neural Text-to-SQL Generation
The sequence-to-sequence paradigm employed by neural text-to-SQL models typically performs token-level decoding and does not consider generating SQL hierarchically from a grammar.
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.
Dissecting Contextual Word Embeddings: Architecture and Representation
Contextual word representations derived from pre-trained bidirectional language models (biLMs) have recently been shown to provide significant improvements to the state of the art for a wide range of NLP tasks.
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.
AllenNLP: A Deep Semantic Natural Language Processing Platform
This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding.
Deep contextualized word representations
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
Ranked #3 on
Only Connect Walls Dataset Task 1 (Grouping)
on OCW
(Wasserstein Distance (WD) metric, using extra
training data)
Citation Intent Classification
Conversational Response Selection
+8
Learning to Reason With Adaptive Computation
Multi-hop inference is necessary for machine learning systems to successfully solve tasks such as Recognising Textual Entailment and Machine Reading.
