Papers by Steven Wilson
Urban Dictionary Embeddings for Slang NLP Applications (2020.lrec-1)
Copied to clipboard
| Challenge: | a new set of word embeddings is released to improve word embedment performance . word embeds provide useful representations of meanings of words in vectors . |
| Approach: | They present a set of word embeddings trained on Urban Dictionary . they show they have high performance across a range of common word embeding evaluations . |
| Outcome: | The first set of word embeddings trained on Urban Dictionary has high performance . the embeddables perform better on a range of common word evaluation tasks . |
Identifying Narrative Content in Podcast Transcripts (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing methods to study narrativity in novels, social media and patient records are limited. |
| Approach: | They propose to process podcast transcripts and extract narrative content from podcasts . they use annotations to enable future research into narrativity within a large corpus of podcast episodes. |
| Outcome: | The proposed methods compare to existing methods and can enable future research into narrativity within a large corpus of approximately 100,000 podcast episodes. |
Predicting Human Activities from User-Generated Content (P19-1)
Copied to clipboard
| Challenge: | Several studies have applied computational approaches to the understanding and modeling of human behavior at scale and in real time. |
| Approach: | They propose a sentence embedding framework tailored to recognize the semantics of human activities and perform automatic clustering of these activities. |
| Outcome: | The proposed framework can make predictions based on the text of user-generated content and self-description. |
Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection (2022.findings-emnlp)
Copied to clipboard
| Challenge: | sarcasm detection datasets focus on intended, rather than perceived sarcasm, but there is no comparison between human and machine performance. |
| Approach: | They collect author-annotated sarcasm datasets that focus on intended, rather than perceived sarcasticism . they compare human-level benchmarks to that of state-of-the-art sarkasmatic detection systems . |
| Outcome: | The proposed datasets compare human and machine performance on sarcastic tasks in English and Arabic. |
Narrative Style and the Spread of Health Misinformation on Twitter (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Using a narrative style is an effective way to communicate health information on and off social media platforms. |
| Approach: | They annotate health misinformation tweets and classify them into narrative and non-narrative . they then use supervised fine-tuning and in-context learning to detect narratives . |
| Outcome: | The proposed model analyzes health misinformation tweets and finds that narrative use is linked to increased tweet engagement and can lead to increased misinformation use. |
Small Town or Metropolis? Analyzing the Relationship between Population Size and Language (2020.lrec-1)
Copied to clipboard
| Challenge: | Prior studies have examined how location affects the type of language that people use . recent electoral results in the united states exemplify a divide in the political opinions of those living in densely populated areas . |
| Approach: | They analyze tweets from different Twitter users to determine whether they are from an urban or rural area. |
| Outcome: | The proposed model trains predictive models to predict whether a user is from an urban or rural area. |
Chandler: An Explainable Sarcastic Response Generator (2021.emnlp-demo)
Copied to clipboard
| Challenge: | sarcasm generators assume intended meaning is opposite of literal meaning . sarcastically generated responses are more specific and coherent to input . |
| Approach: | They propose a system that generates sarcastic responses to a given utterance . they ground their generation process on a formal theory that unambiguously differentiates . |
| Outcome: | The proposed system generates sarcastic responses to a given utterance. |
Representation and Generation of Machine Learning Test Functions (2024.eacl-srw)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have been adopted for ML code generation but their implications are relatively unexplored. |
| Approach: | They examine the use of Large Language Models to extract representations of ML source code and tests to understand the semantic relationships between human-written tests and LLM-generated ones. |
| Outcome: | The proposed models can be used to extract representations of ML source code and tests and annotate them for usefulness, documentation, and correctness. |
Should a Chatbot be Sarcastic? Understanding User Preferences Towards Sarcasm Generation (2022.acl-long)
Copied to clipboard
| Challenge: | sarcasm generation research focused on creating more human-like interactions . previous research focused only on how to generate text that people perceive as sarkastic . |
| Approach: | They propose a theory-driven framework for generating sarcastic responses that allows us to control linguistic devices included during generation. |
| Outcome: | The proposed framework allows us to control the linguistic devices included during generation. |