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Found 30 papers, 9 papers with code
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.
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.
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.
Can LLMs Rank the Harmfulness of Smaller LLMs? We are Not There Yet
Then, we evaluate three state-of-the-art large LLMs on their ability to annotate the harmfulness of these responses.
Joint Training for Selective Prediction
Classifier models are prevalent in natural language processing (NLP), often with high accuracy.
Improving Model Evaluation using SMART Filtering of Benchmark Datasets
To address these concerns, we propose Selection Methodology for Accurate, Reduced, and Targeted (SMART) filtering, a novel approach to select a high-quality subset of examples from existing benchmark datasets by systematically removing less informative and less challenging examples.
Non-Determinism of "Deterministic" LLM Settings
We investigate non-determinism in five LLMs configured to be deterministic when applied to eight common tasks in across 10 runs, in both zero-shot and few-shot settings.
How Well Can You Articulate that Idea? Insights from Automated Formative Assessment
First, we find that the main ideas in the rubric differ with respect to how much freedom they afford in explanations of the idea, thus explanation of a natural law is relatively constrained.
VerAs: Verify then Assess STEM Lab Reports
With an increasing focus in STEM education on critical thinking skills, science writing plays an ever more important role in curricula that stress inquiry skills.
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
We apply CALM to 20 large language models, and find that for 2 language model series, larger parameter models tend to be more biased than smaller ones.
Sociodemographic Bias in Language Models: A Survey and Forward Path
Sociodemographic bias in language models (LMs) has the potential for harm when deployed in real-world settings.
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 #9 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.
