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Found 24 papers, 6 papers with code
Learning Clause Representation from Dependency-Anchor Graph for Connective Prediction
We design a neural model to learn a semantic representation for clauses from graph convolution over latent representations of the subject and verb phrase.
A Semantic Feature-Wise Transformation Relation Network for Automatic Short Answer Grading
Automatic short answer grading (ASAG) is the task of assessing students’ short natural language responses to objective questions.
Contrastive Data and Learning for Natural Language Processing
We then survey the benefits and the best practices of contrastive learning for various downstream NLP applications including Text Classification, Question Answering, Summarization, Text Generation, Interpretability and Explainability, Commonsense Knowledge and Reasoning, Vision-and-Language. This tutorial intends to help researchers in the NLP and computational linguistics community to understand this emerging topic and promote future research directions of using contrastive learning for NLP applications.
The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis
We conduct an inquiry into the sociotechnical aspects of sentiment analysis (SA) by critically examining 189 peer-reviewed papers on their applications, models, and datasets.
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias
To achieve reliability, we introduce the Comprehensive Assessment of Language Model bias (CALM), a benchmark dataset to quantify bias in LMs across three tasks.
Survey on Sociodemographic Bias in Natural Language Processing
In this study, we aim to provide a more comprehensive understanding of the similarities and differences among approaches to sociodemographic bias in NLP.
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning
Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains.
Ranked #5 on
Few-shot NER
on Few-NERD (INTRA)
(using extra training data)
ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences
On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline.
Automated Pyramid Summarization Evaluation
Pyramid evaluation was developed to assess the content of paragraph length summaries of source texts.
Rubric Reliability and Annotation of Content and Argument in Source-Based Argument Essays
We present a unique dataset of student source-based argument essays to facilitate research on the relations between content, argumentation skills, and assessment.
Automated Content Analysis: A Case Study of Computer Science Student Summaries
Technology is transforming Higher Education learning and teaching.
OmniGraph: Rich Representation and Graph Kernel Learning
OmniGraph, a novel representation to support a range of NLP classification tasks, integrates lexical items, syntactic dependencies and frame semantic parses into graphs.
Abstractive Multi-Document Summarization via Phrase Selection and Merging
We propose an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases.
Annotating the MASC Corpus with BabelNet
Finally, we estimate the quality of our annotations using both manually-tagged named entities and word senses, obtaining an accuracy of roughly 70{\%} for both named entities and word sense annotations.
The Benefits of a Model of Annotation
Standard agreement measures for interannotator reliability are neither necessary nor sufficient to ensure a high quality corpus.
Empirical Comparisons of MASC Word Sense Annotations
We analyze how different conceptions of lexical semantics affect sense annotations and how multiple sense inventories can be compared empirically, based on annotated text.
The MASC Word Sense Corpus
The MASC project has produced a multi-genre corpus with multiple layers of linguistic annotation, together with a sentence corpus containing WordNet 3. 1 sense tags for 1000 occurrences of each of 100 words produced by multiple annotators, accompanied by indepth inter-annotator agreement data.
