| Challenge: | In English, more than 70% of documents on the internet contain some form of misspelling . misspellers can be used as prosody to provide additional clues about the writer's attitude . |
| Approach: | They propose two ways to incorporate misspelling semantics into user-generated content . they propose a method to boost micro F1 score by 0.4-2% . |
| Outcome: | The proposed methods can boost the micro F1 score up to 0.4-2% while normalising misspelling is harmful and suboptimal. |
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Context-aware Stand-alone Neural Spelling Correction (2020.findings-emnlp)
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| Challenge: | Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings. |
| Approach: | They propose a stand-alone spelling correction problem that corrects the spelling of tokens without additional token insertion or deletion. |
| Outcome: | The proposed solution outperforms the state-of-the-art spelling correction model by 12.8% absolute F0.5 score. |
Misspelling Detection from Noisy Product Images (2020.coling-industry)
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| Challenge: | Existing spelling research has focused on advancement in misspelling correction . a single inadvertent or intentional misspeller can propagate to large amounts of inventory . |
| Approach: | They propose a method to automatically detect misspellings from product images . they curate a large corpus and define a rich set of features to validate the method . |
| Outcome: | The proposed method improves on a large corpus and an out-of-domain public dataset and improves by 20% in the F1 score. |
NeuSpell: A Neural Spelling Correction Toolkit (2020.emnlp-demos)
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| Challenge: | a new spelling correction toolkit is available for free. |
| Approach: | They propose an open-source toolkit for spelling correction in English . they train neural models using spelling errors in context and using richer contextual representations. |
| Outcome: | The proposed spell-checker improves accuracy on synthetic examples and richer representations of the context. |
Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation (2020.lrec-1)
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| Challenge: | a corpus of 2016 debates and commentary contains 4,648 argumentative propositions annotated with fine-grained proposition types. |
| Approach: | They propose a machine learning-human workflow for annotating for four complex proposition types . they demonstrate with preliminary analysis of rhetorical strategies and structure in presidential debates . |
| Outcome: | The proposed method can be used by technical researchers seeking more nuanced representations of argument . it can also be used to analyze rhetorical strategies and structure in presidential debates . |
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing (D19-60)
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| Challenge: | Workshop on Commonsense Inference in Natural Language Processing focuses on commonsense knowledge representation and application in NLP tasks. |
| Approach: | COIN is a workshop on commonsense inference in natural language processing . workshop included two shared tasks on reading comprehension using commonsensense knowledge . |
| Outcome: | the workshop focused on modeling commonsense knowledge and commonsensing in natural language processing tasks. |
ExPUNations: Augmenting Puns with Keywords and Explanations (2022.emnlp-main)
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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng
| Challenge: | Puns add the challenge of fusing commonsense and world knowledge with the ability to interpret lexical-semantic ambiguity. |
| Approach: | They propose to augment existing datasets with detailed crowdsourced annotations of puns, keywords and fine-grained funniness ratings to challenge current models' ability to understand and generate humor. |
| Outcome: | The proposed tasks include explanation generation to aid with pun classification and keyword-conditioned pun generation to challenge state-of-the-art models' ability to understand and generate humor. |
Spelling-out is not Straightforward: LLMs’ Capability of Tokenization from Token to Characters (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) can spell out tokens character by character with high accuracy, yet struggle with more complex character-level tasks. |
| Approach: | They examine how large language models internally represent character-level information during the spelling-out process. |
| Outcome: | The embedding layer does not fully encode character-level information, especially beyond the first character. |
Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP (D19-59)
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| Challenge: | The first workshop on crowdsourcing for NLP is open to all . |
| Approach: | The first workshop on crowdsourcing annotations for NLP is held at the acl.com . the workshop will focus on methods for aggregating and analysing crowdsourced data for Nl-specific tasks. |
| Outcome: | The first workshop on crowdsourcing for NLP received 16 submissions and accepted 7 . the workshop will focus on ambiguous, subjective or ambiguity analysis of crowdsourced data . |
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts (2021.emnlp-tutorials)
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| Challenge: | EMNLP tutorials are lecture-based presentations that are presented at conferences around the world. |
| Approach: | EMNLP 2021 will feature tutorials on a range of topics, including crowdsourcing and data collection . the final selection process was unified, with a total of 35 submissions . |
| Outcome: | EMNLP 2021 will host six tutorials on a range of topics . the tutorials were selected by a unified review process . |
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
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| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |