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Found 13 papers, 8 papers with code
On Generalization in Coreference Resolution
While coreference resolution is defined independently of dataset domain, most models for performing coreference resolution do not transfer well to unseen domains.
Chess as a Testbed for Language Model State Tracking
Motivated by this issue, we consider the task of language modeling for the game of chess.
Learning Chess Blindfolded
Motivated by this issue, we consider the task of language modeling for the game of chess.
Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks
Long document coreference resolution remains a challenging task due to the large memory and runtime requirements of current models.
Ranked #2 on
Coreference Resolution
on OntoNotes
A Cross-Task Analysis of Text Span Representations
Many natural language processing (NLP) tasks involve reasoning with textual spans, including question answering, entity recognition, and coreference resolution.
PeTra: A Sparsely Supervised Memory Model for People Tracking
We propose PeTra, a memory-augmented neural network designed to track entities in its memory slots.
Ranked #1 on
Coreference Resolution
on GAP
(F1 metric)
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognition
In this paper, we compare a suite of past methods and some of our own proposed methods for using unpaired text data to improve encoder-decoder models.
Hierarchical Multitask Learning for CTC-based Speech Recognition
Previous work has shown that neural encoder-decoder speech recognition can be improved with hierarchical multitask learning, where auxiliary tasks are added at intermediate layers of a deep encoder.
Multilingual Speech Recognition With A Single End-To-End Model
Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the sub-word unit, lexicon and word inventories are typically language specific.
Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information
In conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses.
Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition
We hypothesize that using intermediate representations as auxiliary supervision at lower levels of deep networks may be a good way of combining the advantages of end-to-end training and more traditional pipeline approaches.
Jointly Learning to Align and Convert Graphemes to Phonemes with Neural Attention Models
We propose an attention-enabled encoder-decoder model for the problem of grapheme-to-phoneme conversion.
