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Found 16 papers, 12 papers with code
string2string: A Modern Python Library for String-to-String Algorithms
It includes traditional algorithmic solutions as well as recent advanced neural approaches to tackle various problems in string alignment, distance measurement, lexical and semantic search, and similarity analysis -- along with several helpful visualization tools and metrics to facilitate the interpretation and analysis of these methods.
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages
We introduce three memory-augmented Recurrent Neural Networks (MARNNs) and explore their capabilities on a series of simple language modeling tasks whose solutions require stack-based mechanisms.
Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference
In contrast to standard approaches to NLI, our methods predict the probability of a premise given a hypothesis and NLI label, discouraging models from ignoring the premise.
On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference
Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases.
LSTM Networks Can Perform Dynamic Counting
In this paper, we systematically assess the ability of standard recurrent networks to perform dynamic counting and to encode hierarchical representations.
On Evaluating the Generalization of LSTM Models in Formal Languages
Recurrent Neural Networks (RNNs) are theoretically Turing-complete and established themselves as a dominant model for language processing.
Learning Neural Templates for Text Generation
While neural, encoder-decoder models have had significant empirical success in text generation, there remain several unaddressed problems with this style of generation.
Adapting Sequence Models for Sentence Correction
In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based models and models that encode subword information via convolutions, and that modeling the output data as a series of diffs improves effectiveness over standard approaches.
Challenges in Data-to-Document Generation
Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records.
Word Ordering Without Syntax
Recent work on word ordering has argued that syntactic structure is important, or even required, for effectively recovering the order of a sentence.
Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction
We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016.
Learning Global Features for Coreference Resolution
There is compelling evidence that coreference prediction would benefit from modeling global information about entity-clusters.
Ranked #26 on
Coreference Resolution
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