Papers with modeling approaches
SIMULEVAL: An Evaluation Toolkit for Simultaneous Translation (2020.emnlp-demos)
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| Challenge: | SimulEval is an evaluation toolkit for simultaneous text and speech translation. |
| Approach: | They propose a server-client scheme for simultaneous translation that uses server input and client policies to evaluate models. |
| Outcome: | The proposed evaluation toolkit is available for both text and speech translation. |
A Stylometric Inquiry into Hyperpartisan and Fake News (P18-1)
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| Challenge: | a style analysis of hyperpartisan news and fake news can distinguish them from mainstream news . left-wing and right-wing news share significantly more stylistic similarities than mainstream news does . |
| Approach: | a comparative style analysis of hyperpartisan news and fake news is carried out . authors show that left-wing and right-wing news share significantly more stylistic similarities . |
| Outcome: | a style analysis can distinguish hyperpartisan news from mainstream, satire from both . left-wing and right-wing news share significantly more stylistic similarities than mainstream . |
Consistent Transcription and Translation of Speech (2020.tacl-1)
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| Challenge: | Existing models that translate without transcribing focus on translation quality, while transcription receives less emphasis. |
| Approach: | They propose a method to evaluate consistency and compare different approaches . they propose 'coupled inference' models that feature a coupled inference procedure can achieve strong consistency. |
| Outcome: | The proposed model is poorly suited to the joint transcription/translation task, but is strong enough to train for consistency. |
Investigating Reasons for Disagreement in Natural Language Inference (2022.tacl-1)
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| Challenge: | Several disagreements in natural language inference (NLI) annotation are due to uncertainty in the sentence meaning, others to annotator biases and task artifacts. |
| Approach: | They propose a 4-way classification approach and a multilabel classification approach for detecting disagreements in natural language inference annotations. |
| Outcome: | The proposed model is more expressive and gives better recall of possible interpretations in the data. |
Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender (2022.coling-1)
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| Challenge: | Current modeling of 3rd person pronouns ignores neopronoun phenomena like naive pronounes, which are not (yet) widely established. |
| Approach: | They propose to validate existing and novel approaches for modeling 3rd person pronouns in language technology and validate them through a survey. |
| Outcome: | The proposed model excludes non-binary users, while ignoring gender-specific phenomena. |
Enabling Large Language Models to Generate Text with Citations (2023.emnlp-main)
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| Challenge: | Existing work relies on commercial search engines and human evaluation, making it difficult to reproduce and compare different modeling approaches. |
| Approach: | They propose a new generation paradigm that requires large language models to provide citations to one or a few text passages for any statement they generate. |
| Outcome: | The proposed model improves factual correctness and verifiability of large language models by providing citations to a set of questions and retrieval corpora and generating answers with citation. |
Denoising Large-Scale Image Captioning from Alt-text Data Using Content Selection Models (2022.coling-1)
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| Challenge: | Recent approaches to training large-scale image captioning (IC) models often fall short in terms of performance in leveraging noisy datasets in favor of clean annotations. |
| Approach: | They propose a technique that breaks down the task into two smaller, more controllable tasks - skeleton prediction and skelet-based caption generation. |
| Outcome: | The proposed method can generate better and denoised captions when using noisy datasets. |
MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup (2023.emnlp-main)
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| Challenge: | Disfluency detection models focus on individual utterances, but discontinuities in spoken transcripts occur across multiple turns. |
| Approach: | They propose a multi-turn "cleanup task" to detect discontinuities in spoken conversations . they leverage two modeling approaches for experimental evaluation as benchmarks . |
| Outcome: | The proposed task detects "discontinuities" in spoken conversations that can be removed . the results are compared with existing methods and are expected to be validated in the future . |
Do Question Answering Modeling Improvements Hold Across Benchmarks? (2023.acl-long)
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| Challenge: | a new study finds that human-constructed and downsampled benchmarks hold more concurrence than downsampled benchmarks. |
| Approach: | They propose to measure concurrence between two QA benchmarks on a set of 20 models . they find that human-constructed benchmarks have high concurrence amongst themselves . |
| Outcome: | The proposed models hold broadly across the diverse landscape of question answering (QA) benchmarks. |
A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior (2025.acl-long)
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| Challenge: | Standard models that focus on fixation durations ignore spatial dynamics of reading . authors propose a model that captures how long fixations last, where they land and when . |
| Approach: | They propose a generative model that captures how long fixations last and where they land and when they occur. |
| Outcome: | The proposed model exhibits higher likelihood on held-out reading data than baselines. |