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Found 80 papers, 24 papers with code
Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies
We study the degree to which neural sequence-to-sequence models exhibit fine-grained controllability when performing natural language generation from a meaning representation.
Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation
Large pretrained language models offer powerful generation capabilities, but cannot be reliably controlled at a sub-sentential level.
Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization
Query-focused summaries of foreign-language, retrieved documents can help a user understand whether a document is actually relevant to the query term.
What Do Users Care About? Detecting Actionable Insights from User Feedback
Users often leave feedback on a myriad of aspects of a product which, if leveraged successfully, can help yield useful insights that can lead to further improvements down the line.
Towards Augmenting Lexical Resources for Slang and African American English
Researchers in natural language processing have developed large, robust resources for understanding formal Standard American English (SAE), but we lack similar resources for variations of English, such as slang and African American English (AAE).
Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport
Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged.
Faithfulness-Aware Decoding Strategies for Abstractive Summarization
We present a systematic study of the effect of generation techniques such as beam search and nucleus sampling on faithfulness in abstractive summarization.
Benchmarking Large Language Models for News Summarization
Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood.
Detecting Harmful Agendas in News Articles
Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread.
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization
Missing information is a common issue of dialogue summarization where some information in the reference summaries is not covered in the generated summaries.
In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models
We propose to combine in-context learning objectives with language modeling objectives to distill both the ability to read in-context examples and task knowledge to the smaller models.
Legal and Political Stance Detection of SCOTUS Language
As a natural extension of this political stance detection, we propose the more specialized task of legal stance detection with our new dataset SC-stance, which matches written opinions to legal questions.
CREATIVESUMM: Shared Task on Automatic Summarization for Creative Writing
We detail the process of curating these datasets for the task, as well as the metrics used for the evaluation of the submissions.
Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection
Summarizing novel chapters is a difficult task due to the input length and the fact that sentences that appear in the desired summaries draw content from multiple places throughout the chapter.
SafeText: A Benchmark for Exploring Physical Safety in Language Models
Understanding what constitutes safe text is an important issue in natural language processing and can often prevent the deployment of models deemed harmful and unsafe.
Mitigating Covertly Unsafe Text within Natural Language Systems
An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threatening consequences.
On the Relation between Sensitivity and Accuracy in In-context Learning
In-context learning (ICL) suffers from oversensitivity to the prompt, making it unreliable in real-world scenarios.
Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions
Generics express generalizations about the world (e. g., birds can fly) that are not universally true (e. g., newborn birds and penguins cannot fly).
Seeded Hierarchical Clustering for Expert-Crafted Taxonomies
Practitioners from many disciplines (e. g., political science) use expert-crafted taxonomies to make sense of large, unlabeled corpora.
Learning to Revise References for Faithful Summarization
In real-world scenarios with naturally occurring datasets, reference summaries are noisy and may contain information that cannot be inferred from the source text.
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization
A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document.
Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation
While there has been a lot of research and many recent advances in neural fake news detection, defending against human-written disinformation remains underexplored.
An analysis of document graph construction methods for AMR summarization
Prior work in AMR-based summarization has automatically merged the individual sentence graphs into a document graph, but the method of merging and its effects on summary content selection have not been independently evaluated.
A Bag of Tricks for Dialogue Summarization
Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization.
Semantic Categorization of Social Knowledge for Commonsense Question Answering
Large pre-trained language models (PLMs) have led to great success on various commonsense question answering (QA) tasks in an end-to-end fashion.
Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization
Despite recent progress in abstractive summarization, systems still suffer from faithfulness errors.
Event-Centric Natural Language Processing
This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text.
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection
To defend against machine-generated fake news, an effective mechanism is urgently needed.
Cross-language Sentence Selection via Data Augmentation and Rationale Training
This paper proposes an approach to cross-language sentence selection in a low-resource setting.
Emotion-Infused Models for Explainable Psychological Stress Detection
The problem of detecting psychological stress in online posts, and more broadly, of detecting people in distress or in need of help, is a sensitive application for which the ability to interpret models is vital.
Adversarial Learning for Zero-Shot Stance Detection on Social Media
Stance detection on social media can help to identify and understand slanted news or commentary in everyday life.
Improving Factual Consistency of Abstractive Summarization via Question Answering
A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents.
Segmenting Subtitles for Correcting ASR Segmentation Errors
Typical ASR systems segment the input audio into utterances using purely acoustic information, which may not resemble the sentence-like units that are expected by conventional machine translation (MT) systems for Spoken Language Translation.
Supporting Clustering with Contrastive Learning
Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space.
Ranked #1 on
Short Text Clustering
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Entity-level Factual Consistency of Abstractive Text Summarization
A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document.
Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings
We propose a method for online news stream clustering that is a variant of the non-parametric streaming K-means algorithm.
Event Guided Denoising for Multilingual Relation Learning
General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks.
Event-Guided Denoising for Multilingual Relation Learning
General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks.
Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages
We adopt cross-lingual embeddings constructed using different methods to extract features of the tweets, including a few state-of-the-art contextual embeddings such as BERT, RoBERTa and XLM-R. We train classifiers of different architectures on the extracted features.
Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling
Unsupervised domain adaptation addresses the problem of leveraging labeled data in a source domain to learn a well-performing model in a target domain where labels are unavailable.
Cross-Lingual Document Classification
Document Classification
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Incorporating Terminology Constraints in Automatic Post-Editing
In this paper, we present both autoregressive and non-autoregressive models for lexically constrained APE, demonstrating that our approach enables preservation of 95% of the terminologies and also improves translation quality on English-German benchmarks.
Subtitles to Segmentation: Improving Low-Resource Speech-to-Text Translation Pipelines
In this work, we focus on improving ASR output segmentation in the context of low-resource language speech-to-text translation.
WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization
As a set of baselines for further studies, we evaluate the performance of existing cross-lingual abstractive summarization methods on our dataset.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+2
Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations
Stance detection is an important component of understanding hidden influences in everyday life.
A Unified Feature Representation for Lexical Connotations
Ideological attitudes and stance are often expressed through subtle meanings of words and phrases.
Exploring Content Selection in Summarization of Novel Chapters
We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides.
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations.
A Good Sample is Hard to Find: Noise Injection Sampling and Self-Training for Neural Language Generation Models
Deep neural networks (DNN) are quickly becoming the de facto standard modeling method for many natural language generation (NLG) tasks.
Dreaddit: A Reddit Dataset for Stress Analysis in Social Media
Stress is a nigh-universal human experience, particularly in the online world.
Automatically Inferring Gender Associations from Language
In this paper, we pose the question: do people talk about women and men in different ways?
Fine-grained Sentiment Analysis with Faithful Attention
While the general task of textual sentiment classification has been widely studied, much less research looks specifically at sentiment between a specified source and target.
The ARIEL-CMU Systems for LoReHLT18
This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).
Content Selection in Deep Learning Models of Summarization
We carry out experiments with deep learning models of summarization across the domains of news, personal stories, meetings, and medical articles in order to understand how content selection is performed.
Predictive Embeddings for Hate Speech Detection on Twitter
We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular.
Detecting Gang-Involved Escalation on Social Media Using Context
Gang-involved youth in cities such as Chicago have increasingly turned to social media to post about their experiences and intents online.
Multimodal Social Media Analysis for Gang Violence Prevention
In this paper we partnered computer scientists with social work researchers, who have domain expertise in gang violence, to analyze how public tweets with images posted by youth who mention gang associations on Twitter can be leveraged to automatically detect psychosocial factors and conditions that could potentially assist social workers and violence outreach workers in prevention and early intervention programs.
Domain-Adaptable Hybrid Generation of RDF Entity Descriptions
RDF ontologies provide structured data on entities in many domains and continue to grow in size and diversity.
Leveraging Sparse and Dense Feature Combinations for Sentiment Classification
This model outperforms many deep learning models and achieves comparable results to other deep learning models with complex architectures on sentiment analysis datasets.
SMARTies: Sentiment Models for Arabic Target Entities
We consider entity-level sentiment analysis in Arabic, a morphologically rich language with increasing resources.
Automatically Processing Tweets from Gang-Involved Youth: Towards Detecting Loss and Aggression
Violence is a serious problems for cities like Chicago and has been exacerbated by the use of social media by gang-involved youths for taunting rival gangs.
Using Natural Language Processing and Qualitative Analysis to Intervene in Gang Violence: A Collaboration Between Social Work Researchers and Data Scientists
The U. S. has the highest rate of firearm-related deaths when compared to other industrialized countries.
Real-Time Web Scale Event Summarization Using Sequential Decision Making
We present a system based on sequential decision making for the online summarization of massive document streams, such as those found on the web.
Annotating Agreement and Disagreement in Threaded Discussion
We introduce a new corpus of sentence-level agreement and disagreement annotations over LiveJournal and Wikipedia threads.
