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Found 66 papers, 19 papers with code
TWEETSPIN: Fine-grained Propaganda Detection in Social Media Using Multi-View Representations
Our model relies on multi-view representations of the input tweet data to (a) extract different aspects of the input text including the context, entities, their relationships, and external knowledge; (b) model their mutual interplay; and (c) effectively speed up the learning process by requiring fewer training examples.
Aligning Generative Language Models with Human Values
Although current large-scale generative language models (LMs) can show impressive insights about factual knowledge, they do not exhibit similar success with respect to human values judgements (e. g., whether or not the generations of an LM are moral).
DartmouthCS at SemEval-2022 Task 8: Predicting Multilingual News Article Similarity with Meta-Information and Translation
This paper presents our approach for tackling SemEval-2022 Task 8: Multilingual News Article Similarity.
Dartmouth at SemEval-2022 Task 6: Detection of Sarcasm
This paper introduces the result of Team Dartmouth’s experiments on each of the five subtasks for the detection of sarcasm in English and Arabic tweets.
Multi-resolution Annotations for Emoji Prediction
The lack of multi-label and aspect-level emoji prediction datasets is one of the bottlenecks for this task.
Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts
Aspect-based summarization has seen significant advancements, especially in structured text.
Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Models
Large Language Models (LLMs) have significantly advanced the field of Natural Language Processing (NLP), but their lack of interpretability has been a major concern.
Improving Representation Learning for Histopathologic Images with Cluster Constraints
This framework aims for transferable representation learning and semantically meaningful clustering by synergizing invariance loss and clustering loss in WSI analysis.
Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction
Our experimental findings demonstrate that our approach accelerates the training of vision-language models by a factor of 5 without a noticeable impact on overall performance.
Bootstrapping Vision-Language Learning with Decoupled Language Pre-training
We present a novel methodology aimed at optimizing the application of frozen large language models (LLMs) for resource-intensive vision-language (VL) pre-training.
Joint Latent Topic Discovery and Expectation Modeling for Financial Markets
In the pursuit of accurate and scalable quantitative methods for financial market analysis, the focus has shifted from individual stock models to those capturing interrelations between companies and their stocks.
Graph-Level Embedding for Time-Evolving Graphs
We evaluate our proposed model on five publicly available datasets for the task of temporal graph similarity ranking, and our model outperforms baseline methods.
Training Socially Aligned Language Models on Simulated Social Interactions
Social alignment in AI systems aims to ensure that these models behave according to established societal values.
Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the Data-Scarce RNA Contact Prediction Task
Our findings indicate that the learned structural patterns of proteins can be transferred to RNAs, opening up potential new avenues for research.
Capturing Topic Framing via Masked Language Modeling
Differential framing of issues can lead to divergent world views on important issues.
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits
We present Second Thought, a new learning paradigm that enables language models (LMs) to re-align with human values.
Mind's Eye: Grounded Language Model Reasoning through Simulation
By training solely on written text, current language models (LMs) miss the grounded experience of humans in the real-world -- their failure to relate language to the physical world causes knowledge to be misrepresented and obvious mistakes in their reasoning.
Language Models are Multilingual Chain-of-Thought Reasoners
Finally, we show that the multilingual reasoning abilities of language models extend to other tasks such as commonsense reasoning and word-in-context semantic judgment.
Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings
This indicates that Transformer models are able to generalize better by doing a similar task (i. e., clustering) with unpaired examples from different modalities in a multi-task fashion.
Robin: A Novel Online Suicidal Text Corpus of Substantial Breadth and Scale
Suicide is a major public health crisis.
Interpretation Quality Score for Measuring the Quality of interpretability methods
Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) tasks in recent years.
Contrastive Learning for Prompt-Based Few-Shot Language Learners
Following this line of work, we present a contrastive learning framework that clusters inputs from the same class for better generality of models trained with only limited examples.
Embedding Hallucination for Few-Shot Language Fine-tuning
Few-shot language learners adapt knowledge from a pre-trained model to recognize novel classes from a few-labeled sentences.
Non-Parallel Text Style Transfer with Self-Parallel Supervision
The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained.
Knowledge Infused Decoding
Hence, they tend to suffer from counterfactual or hallucinatory generation when used in knowledge-intensive natural language generation (NLG) tasks.
Ranked #2 on
Question Answering
on KILT: ELI5
Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning
Artificial intelligence, particularly through recent advancements in deep learning, has achieved exceptional performances in many tasks in fields such as natural language processing and computer vision.
EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English
While cultural backgrounds have been shown to affect linguistic expressions, existing natural language processing (NLP) research on culture modeling is overly coarse-grained and does not examine cultural differences among speakers of the same language.
Cultural Vocal Bursts Intensity Prediction
Language Modelling
+5
Emotion-based Modeling of Mental Disorders on Social Media
Our proposed model is different from other work in this area in that our model is based entirely on the emotional states, and the transition between these states of users on Reddit, whereas prior work is typically based on content-based representations (e. g., n-grams, language model embeddings, etc).
Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization
Recent years have seen a rise in the development of representational learning methods for graph data.
Embedding Node Structural Role Identity Using Stress Majorization
In this paper, we present a novel and flexible framework using stress majorization, to transform the high-dimensional role identities in networks directly (without approximation or indirect modeling) to a low-dimensional embedding space.
GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks
Compared to AUTOSEM, a strong baseline method, GradTS improves the performance of MT-DNN with a bert-base-cased backend model, from 0. 33% to 17. 93% on 8 natural language understanding (NLU) tasks in the GLUE benchmarks.
Language Model Augmented Relevance Score
Although automated metrics are commonly used to evaluate NLG systems, they often correlate poorly with human judgements.
Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks
Through extensive experiments, we show that (1) pruning a number of attention heads in a multi-lingual Transformer-based model has, in general, positive effects on its performance in cross-lingual and multi-lingual tasks and (2) the attention heads to be pruned can be ranked using gradients and identified with a few trial experiments.
Modulating Language Models with Emotions
Generating context-aware language that embodies diverse emotions is an important step towards building empathetic NLP systems.
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path
A common type of network is the heterogeneous network, where the nodes (and edges) can be of different types.
Linguistic Complexity Loss in Text-Based Therapy
The recent advent of online text-based therapy presents a new opportunity to analyze the complexity loss paradox in a novel operationalization: linguistic complexity loss in text-based therapy conversations.
A Survey of Data Augmentation Approaches for NLP
In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner.
Mitigating Political Bias in Language Models Through Reinforced Calibration
Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world settings.
BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly Models
This paper describes a system submitted by team BigGreen to LCP 2021 for predicting the lexical complexity of English words in a given context.
Lone Pine at SemEval-2021 Task 5: Fine-Grained Detection of Hate Speech Using BERToxic
This paper describes our approach to the Toxic Spans Detection problem (SemEval-2021 Task 5).
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning
Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category.
Feature Selection for Multivariate Time Series via Network Pruning
In recent years, there has been an ever increasing amount of multivariate time series (MTS) data in various domains, typically generated by a large family of sensors such as wearable devices.
Text Augmentation in a Multi-Task View
Traditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion where augmented examples dominate training.
Political Depolarization of News Articles Using Attribute-aware Word Embeddings
To detect polar words, we train a multi-attribute-aware word embedding model that is aware of ideology and topics on 360k full-length media articles.
Social media data reveals signal for public consumer perceptions
By using decadal data (2008-2019) from Reddit, we show that both monthly and daily estimates of CCI can, indeed, be reliably estimated at least several months in advance, and that our model estimates are far superior to those generated by the existing methods.
Multi-modal Identification of State-Sponsored Propaganda on Social Media
The prevalence of state-sponsored propaganda on the Internet has become a cause for concern in the recent years.
Big Green at WNUT 2020 Shared Task-1: Relation Extraction as Contextualized Sequence Classification
Relation and event extraction is an important task in natural language processing.
Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet Classification Using BERT
We compared its performance to a suite of machine learning models.
Improvements and Extensions on Metaphor Detection
Finally, we clean up the improper or outdated annotations in one of the MD benchmark datasets and re-benchmark it with our Transformer-based model.
An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data
The field of NLP has seen unprecedented achievements in recent years.
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation
For instance, Data Boost improves F1 for the three tasks by 8. 7% on average when given only 10% of the whole data for training.
Enhanced Offensive Language Detection Through Data Augmentation
In this work, we present Dager (Data Augmenter), a generation-based data augmentation method, that improves the performance of classification on imbalanced and low-resource data such as the offensive language dataset.
Embedding Node Structural Role Identity into Hyperbolic Space
However, the work on network embedding in hyperbolic space has been focused on microscopic node embedding.
Towards Improved Model Design for Authorship Identification: A Survey on Writing Style Understanding
While other tasks based on linguistic style understanding benefit from deep learning methods, these methods have not behaved as well as traditional machine learning methods in many authorship-based tasks.
Emoji Prediction: Extensions and Benchmarking
In this paper, we extend the existing setting of the emoji prediction task to include a richer set of emojis and to allow multi-label classification on the task.
Query-Free Adversarial Transfer via Undertrained Surrogates
Deep neural networks are vulnerable to adversarial examples -- minor perturbations added to a model's input which cause the model to output an incorrect prediction.
Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks
Once trained on their accounts, users can have new photos sorted based on predicted engagement and style similarity to their previous work, thus enabling them to upload photos that not only have the potential to maximize engagement from their followers but also maintain their style of photography.
What Are People Asking About COVID-19? A Question Classification Dataset
We present COVID-Q, a set of 1, 690 questions about COVID-19 from 13 sources, which we annotate into 15 question categories and 207 question clusters.
Twitter Demographic Classification Using Deep Multi-modal Multi-task Learning
In this paper, we present a demographic classifier for gender, age, political orientation and location on Twitter.
Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder
The vector representations generated by our model are generic, and hence can be applied to a variety of tasks.
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
This paper describes our approach for the Detecting Stance in Tweets task (SemEval-2016 Task 6).
Automatic Detection and Categorization of Election-Related Tweets
With the rise in popularity of public social media and micro-blogging services, most notably Twitter, the people have found a venue to hear and be heard by their peers without an intermediary.
A Semi-automatic Method for Efficient Detection of Stories on Social Media
In this paper, we present a novel semi-automatic tool that enables users to efficiently identify and track stories about real-world events on Twitter.
Digital Stylometry: Linking Profiles Across Social Networks
There is an ever growing number of users with accounts on multiple social media and networking sites.
Enhanced Twitter Sentiment Classification Using Contextual Information
This combined classifier outperforms the purely linguistic classifier, showing that integrating the rich contextual information available on Twitter into sentiment classification is a promising direction of research.
Tweet Acts: A Speech Act Classifier for Twitter
We created a taxonomy of six speech acts for Twitter and proposed a set of semantic and syntactic features.
