Search Results for author:
Found 11 papers, 8 papers with code
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
Knowledge and language understanding of models evaluated through question answering (QA) has been usually studied on static snapshots of knowledge, like Wikipedia.
Mogrifier LSTM
Many advances in Natural Language Processing have been based upon more expressive models for how inputs interact with the context in which they occur.
Encoding Spatial Relations from Natural Language
Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world.
Pushing the bounds of dropout
We show that dropout training is best understood as performing MAP estimation concurrently for a family of conditional models whose objectives are themselves lower bounded by the original dropout objective.
Ranked #24 on
Language Modelling
on Penn Treebank (Word Level)
The NarrativeQA Reading Comprehension Challenge
Reading comprehension (RC)---in contrast to information retrieval---requires integrating information and reasoning about events, entities, and their relations across a full document.
Ranked #9 on
Question Answering
on NarrativeQA
(BLEU-1 metric)
Dynamic Integration of Background Knowledge in Neural NLU Systems
Common-sense and background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, this knowledge must be acquired from training corpora during learning, and then it is static at test time.
Ranked #34 on
Question Answering
on TriviaQA
Semantic Parsing with Semi-Supervised Sequential Autoencoders
We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing.
Latent Predictor Networks for Code Generation
Many language generation tasks require the production of text conditioned on both structured and unstructured inputs.
Ranked #10 on
Code Generation
on Django
Reasoning about Entailment with Neural Attention
We extend this model with a word-by-word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases.
Ranked #83 on
Natural Language Inference
on SNLI
Teaching Machines to Read and Comprehend
Teaching machines to read natural language documents remains an elusive challenge.
Ranked #13 on
Question Answering
on CNN / Daily Mail
Learning Bilingual Word Representations by Marginalizing Alignments
We present a probabilistic model that simultaneously learns alignments and distributed representations for bilingual data.
