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Found 34 papers, 23 papers with code
Don’t Go Far Off: An Empirical Study on Neural Poetry Translation
Moreover, multilingual fine-tuning on poetic data outperforms bilingual fine-tuning on poetic data.
Identifying Distributional Perspectives from Colingual Groups
Discrepancies exist among different cultures or languages.
Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts
In the last decade, the United States has lost more than 500, 000 people from an overdose involving prescription and illicit opioids making it a national public health emergency (USDHHS, 2017).
Learning to Follow Object-Centric Image Editing Instructions Faithfully
Current approaches focusing on image editing with natural language instructions rely on automatically generated paired data, which, as shown in our investigation, is noisy and sometimes nonsensical, exacerbating the above issues.
Art or Artifice? Large Language Models and the False Promise of Creativity
Inspired by the Torrance Test of Creative Thinking (TTCT), which measures creativity as a process, we use the Consensual Assessment Technique [3] and propose the Torrance Test of Creative Writing (TTCW) to evaluate creativity as a product.
Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers
The development of large language models (LLMs) capable of following instructions and engaging in conversational interactions sparked increased interest in their utilization across various support tools.
I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors
We propose to solve the task through the collaboration between Large Language Models (LLMs) and Diffusion Models: Instruct GPT-3 (davinci-002) with Chain-of-Thought prompting generates text that represents a visual elaboration of the linguistic metaphor containing the implicit meaning and relevant objects, which is then used as input to the diffusion-based text-to-image models. Using a human-AI collaboration framework, where humans interact both with the LLM and the top-performing diffusion model, we create a high-quality dataset containing 6, 476 visual metaphors for 1, 540 linguistic metaphors and their associated visual elaborations.
Multitask Instruction-based Prompting for Fallacy Recognition
To move towards solving the fallacy recognition task, we approach these differences across datasets as multiple tasks and show how instruction-based prompting in a multitask setup based on the T5 model improves the results against approaches built for a specific dataset such as T5, BERT or GPT-3.
Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing
The core component of our system is a language model fine-tuned on a diverse collection of instructions for poetry writing.
CONSISTENT: Open-Ended Question Generation From News Articles
Recent work on question generation has largely focused on factoid questions such as who, what, where, when about basic facts.
Fine-tuned Language Models are Continual Learners
In spite of the limited success of Continual Learning we show that Language Models can be continual learners.
FLUTE: Figurative Language Understanding through Textual Explanations
Figurative language understanding has been recently framed as a recognizing textual entailment (RTE) task (a. k. a.
Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models
Enthymemes are defined as arguments where a premise or conclusion is left implicit.
Don't Go Far Off: An Empirical Study on Neural Poetry Translation
Moreover, multilingual fine-tuning on poetic data outperforms \emph{bilingual} fine-tuning on poetic data.
It's not Rocket Science : Interpreting Figurative Language in Narratives
Figurative language is ubiquitous in English.
COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic
The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence.
Metaphor Generation with Conceptual Mappings
Guided by conceptual metaphor theory, we propose to control the generation process by encoding conceptual mappings between cognitive domains to generate meaningful metaphoric expressions.
Figurative Language in Recognizing Textual Entailment
We introduce a collection of recognizing textual entailment (RTE) datasets focused on figurative language.
ENTRUST: Argument Reframing with Language Models and Entailment
Framing involves the positive or negative presentation of an argument or issue depending on the audience and goal of the speaker (Entman 1983).
MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding
Generating metaphors is a challenging task as it requires a proper understanding of abstract concepts, making connections between unrelated concepts, and deviating from the literal meaning.
DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation
Having engaging and informative conversations with users is the utmost goal for open-domain conversational systems.
Content Planning for Neural Story Generation with Aristotelian Rescoring
Long-form narrative text generated from large language models manages a fluent impersonation of human writing, but only at the local sentence level, and lacks structure or global cohesion.
Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation
We also show how replacing literal sentences with similes from our best model in machine generated stories improves evocativeness and leads to better acceptance by human judges.
R\^3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge
We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence.
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
Our approach for relation prediction uses contextual information in terms of fine-tuning a pre-trained language model and leveraging discourse relations based on Rhetorical Structure Theory.
$R^3$: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge
We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence.
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking
The increased focus on misinformation has spurred development of data and systems for detecting the veracity of a claim as well as retrieving authoritative evidence.
Identifying Distributional Perspective Differences from Colingual Groups
Perspective differences exist among different cultures or languages.
Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks
Word pairs across argument spans have been shown to be effective for predicting the discourse relation between them.
Pay ``Attention'' to your Context when Classifying Abusive Language
The goal of any social media platform is to facilitate healthy and meaningful interactions among its users.
ColumbiaNLP at SemEval-2019 Task 8: The Answer is Language Model Fine-tuning
Community Question Answering forums are very popular nowadays, as they represent effective means for communities to share information around particular topics.
Robust Document Retrieval and Individual Evidence Modeling for Fact Extraction and Verification.
Our team finished 6th out of 24 teams on the leader-board based on the preliminary results with a FEVER score of 49. 06 on the blind test set compared to 27. 45 of the baseline system.
Context-Aware Attention for Understanding Twitter Abuse
The original goal of any social media platform is to facilitate users to indulge in healthy and meaningful conversations.
