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Found 14 papers, 7 papers with code
Constrained Fact Verification for FEVER
Fact-verification systems are well explored in the NLP literature with growing attention owing to shared tasks like FEVER.
Comparing Grammatical Theories of Code-Mixing
Code-mixed text generation systems have found applications in many downstream tasks, including speech recognition, translation and dialogue.
Team JARS: DialDoc Subtask 1 - Improved Knowledge Identification with Supervised Out-of-Domain Pretraining
In this paper, we discuss our submission for DialDoc subtask 1.
A Study of Morphological Robustness of Neural Machine Translation
In this work, we analyze the robustness of neural machine translation systems towards grammatical perturbations in the source.
Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing
In this paper, we present CASENT, a seq2seq model designed for ultra-fine entity typing that predicts ultra-fine types with calibrated confidence scores.
Background Summarization of Event Timelines
Generating concise summaries of news events is a challenging natural language processing task.
Hierarchical Event Grounding
In this work, we present an extension to the event grounding task that requires tackling hierarchical event structures from the KB.
Multilingual Event Linking to Wikidata
On the two proposed tasks, we compare multiple event linking systems including BM25+ (Lv and Zhai, 2011) and multilingual adaptations of the biencoder and crossencoder architectures from BLINK (Wu et al., 2020).
Cross-document Event Identity via Dense Annotation
To this end, we design a new annotation workflow with careful quality control and an easy-to-use annotation interface.
Evaluating the Morphosyntactic Well-formedness of Generated Texts
Text generation systems are ubiquitous in natural language processing applications.
Automatic Extraction of Rules Governing Morphological Agreement
Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data.
Word Embeddings for Code-Mixed Language Processing
We compare three existing bilingual word embedding approaches, and a novel approach of training skip-grams on synthetic code-mixed text generated through linguistic models of code-mixing, on two tasks - sentiment analysis and POS tagging for code-mixed text.
Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data
Training language models for Code-mixed (CM) language is known to be a difficult problem because of lack of data compounded by the increased confusability due to the presence of more than one language.
Automatic Speech Recognition (ASR)
Language Identification
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