| Nome |
# |
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Pipelines for social bias testing of large language models, file f7980a13-e6ba-4f66-8311-4f3f1a3626bc
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1.354
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The importance of modeling social factors of language: theory and practice, file e31e10d4-7912-31fb-e053-1705fe0a5b99
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1.104
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Measuring harmful sentence completion in language models for LGBTQIA+ individuals, file 442bf186-670e-40a4-93dd-71f79640d8f0
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903
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HONEST: measuring hurtful sentence completion in language models, file e31e10d4-7910-31fb-e053-1705fe0a5b99
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785
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SafetyKit: first aid for measuring safety in open-domain conversational systems, file 9ee7908c-a74f-4bca-9ece-a2e00db5f1b6
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751
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Beyond black & white: leveraging annotator disagreement via soft-label multi-task learning, file e31e10d4-6f0e-31fb-e053-1705fe0a5b99
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695
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FEEL-IT: emotion and sentiment classification for the Italian language, file e31e10d4-7b51-31fb-e053-1705fe0a5b99
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619
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Multitask learning for mental health conditions with limited social media data, file e31e10d4-aad6-31fb-e053-1705fe0a5b99
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522
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Five sources of bias in natural language processing, file e31e10d4-7639-31fb-e053-1705fe0a5b99
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440
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BERTective: language models and contextual information for deception detection, file e31e10d4-72fc-31fb-e053-1705fe0a5b99
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397
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Benchmarking post-hoc interpretability approaches for transformer-based misogyny detection, file dd2e13b2-7991-45ef-93ce-3a0e94a7d859
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382
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Guiding the release of safer E2E conversational AI through value sensitive design, file dc4c9d96-9359-4ff5-bf49-00e445022e9e
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354
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Respectful or toxic? Using zero-shot learning with language models to detect hate speech, file 4a8fb2c4-30d6-487c-acd5-2e075034cea1
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268
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Learning a POS tagger for AAVE-like language, file e31e10d4-a9dd-31fb-e053-1705fe0a5b99
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255
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Universal joy: a data set and results for classifying emotions across languages, file e31e10d4-7e5b-31fb-e053-1705fe0a5b99
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180
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Hey Siri. Ok Google. Alexa: a topic modeling of user reviews for smart speakers, file e31e10d3-c1e5-31fb-e053-1705fe0a5b99
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149
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Geolocation with attention-based multitask learning models, file e31e10d3-c1eb-31fb-e053-1705fe0a5b99
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143
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Pre-training is a hot topic: contextualized document embeddings improve topic coherence, file e31e10d4-790e-31fb-e053-1705fe0a5b99
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123
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“We will reduce taxes” - Identifying election pledges with language models, file e31e10d4-72ff-31fb-e053-1705fe0a5b99
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117
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The state of profanity obfuscation in Natural Language Processing scientific publications, file c5e8ed0f-cc25-4607-bc74-a66a5c9ed0bb
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112
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Identifying linguistic areas for geolocation, file e31e10d3-c1e9-31fb-e053-1705fe0a5b99
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96
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Predicting news headline popularity with syntactic and semantic knowledge using multi-task learning, file e31e10d3-87bf-31fb-e053-1705fe0a5b99
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94
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MilaNLP @ WASSA: does BERT feel sad when you cry?, file e31e10d4-72fd-31fb-e053-1705fe0a5b99
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82
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Hard and soft evaluation of NLP models with BOOtSTrap SAmpling - BooStSa, file 930704e4-734f-4c98-b1f6-cb82f9429863
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73
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Dense node representation for geolocation, file e31e10d3-c1ee-31fb-e053-1705fe0a5b99
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68
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Can demographic factors improve text classification? Revisiting demographic adaptation in the age of transformers, file b7eafdbf-df44-43a5-8e02-628518046121
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67
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Increasing in-class similarity by retrofitting embeddings with demographic information, file e31e10d3-c57f-31fb-e053-1705fe0a5b99
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64
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Comparing Bayesian models of annotation, file e31e10d3-c63a-31fb-e053-1705fe0a5b99
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64
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Helpful or hierarchical? Predicting the communicative strategies of chat participants, and their impact on success, file e31e10d4-27ef-31fb-e053-1705fe0a5b99
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64
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Women’s syntactic resilience and men’s grammatical luck: gender-bias in part-of-speech tagging and dependency parsing, file e31e10d3-c570-31fb-e053-1705fe0a5b99
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60
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The social and the neural network: how to make natural language processing about people again, file e31e10d3-c650-31fb-e053-1705fe0a5b99
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55
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A case for soft loss functions, file e31e10d4-2923-31fb-e053-1705fe0a5b99
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54
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Predictive biases in natural language processing models: a conceptual framework and overview, file e31e10d4-2815-31fb-e053-1705fe0a5b99
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52
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“You sound just like your father”. Commercial machine translation systems include stylistic biases, file e31e10d4-2817-31fb-e053-1705fe0a5b99
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44
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Capturing regional variation with distributed place representations and geographic retrofitting, file e31e10d3-87be-31fb-e053-1705fe0a5b99
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43
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Welcome to the modern world of pronouns: identity-inclusive Natural Language Processing beyond gender, file 5caed632-82d4-4df3-be1b-b2f847723b5f
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42
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We need to consider disagreement in evaluation, file e31e10d4-725c-31fb-e053-1705fe0a5b99
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30
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On the gap between adoption and understanding in NLP, file e31e10d4-7658-31fb-e053-1705fe0a5b99
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29
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Viewpoint: Artificial Intelligence accidents waiting to happen?, file c8c718f4-1002-4b8b-bd8e-864a53691737
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28
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Learning from disagreement: a survey, file ac01011f-c459-4daf-8aeb-5793b0bcade5
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27
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Entropy-based attention regularization frees unintended bias mitigation from lists, file f9bd81ff-903a-4b7e-9143-5b7c5089b7a0
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27
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Cross-lingual contextualized topic models with zero-shot learning, file e31e10d4-7656-31fb-e053-1705fe0a5b99
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26
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Two contrasting data annotation paradigms for subjective NLP tasks, file 0600cf7e-52fb-406f-8b62-e7a616b017cf
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22
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MilaNLP at SemEval-2023 Task 10: ensembling domain-adapted and regularized pretrained language models for robust sexism detection, file 00d41fe7-67a1-435b-8dee-1f44c7bb4b17
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21
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Language invariant properties in Natural Language Processing, file 6d2bf7d9-300a-43e6-84de-cd025b325581
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20
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"It's not just hate": a multi-dimensional perspective on detecting harmful speech online, file d1b46c69-10c2-4990-9a44-f38dc13ab2e7
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19
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XLM-EMO: multilingual emotion prediction in social media text, file dc06b5ac-3cac-4f2d-a7c3-10282ba182f3
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18
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SocioProbe: what, when, and where language models learn about sociodemographics, file 92414e9d-a58b-4254-8877-9c6387b8a215
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14
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The ecological fallacy in annotation: modeling human label variation goes beyond sociodemographics, file fec1c2c6-8690-4f74-85ac-ffffbe380572
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14
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Data-efficient strategies for expanding hate speech detection into under-resourced languages, file 3dde6ad8-fcac-4b61-a106-7f4147585a50
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11
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Bridging fairness and environmental sustainability in natural language processing, file dd6626f8-6544-458a-af48-9c5d4afbc429
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11
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Twitter-demographer: a flow-based tool to enrich Twitter data, file 1db52899-8354-4385-9c10-db459eb32546
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10
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Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter, file ed4adf69-baec-46a2-b738-59d4947df732
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8
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What about em? How commercial Machine Translation fails to handle (neo-)pronouns, file 07523bb3-8226-4d9d-b9cf-0e63ea43546e
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7
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What’s in a p-value in NLP?, file e31e10d3-1ce1-31fb-e053-1705fe0a5b99
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2
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Lörres, Möppes, and the Swiss. (Re)Discovering regional patterns in anonymous social media data, file e31e10d3-c1e1-31fb-e053-1705fe0a5b99
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2
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Beyond digital "echo chambers": the role of viewpoint diversity in political discussion, file 22f71c78-d9b4-47fd-bcb9-df853af42e20
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1
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Demographic factors improve classification performance, file bc37e30f-a50f-4a93-bcd4-5477aa4f95d1
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1
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A Walk-Based Semantically Enriched Tree Kernel Over Distributed Word Representations, file e31e10d3-2185-31fb-e053-1705fe0a5b99
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1
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Learning whom to trust with MACE, file e31e10d3-2187-31fb-e053-1705fe0a5b99
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1
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Augmenting English adjective senses with supersenses, file e31e10d3-225c-31fb-e053-1705fe0a5b99
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1
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Text analysis in Python for social scientists: discovery and exploration, file e31e10d4-27ec-31fb-e053-1705fe0a5b99
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1
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Wordify: a tool for discovering and differentiating consumer vocabularies, file e31e10d4-7ab9-31fb-e053-1705fe0a5b99
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1
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Text analysis in Python for social scientists : prediction and classification, file e31e10d4-a6bc-31fb-e053-1705fe0a5b99
|
1
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| Totale |
11.029 |