Papers with noisy text
Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking (D19-55)
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Nathanael Chambers, Timothy Forman, Catherine Griswold, Kevin Lu, Yogaish Khastgir, Stephen Steckler
| Challenge: | Illicit activity on the Web often obscures information between client and seller, such as the seller’s phone number. |
| Approach: | They propose to use a dataset to model adversarial noise in a text extraction system and propose a visual character language model to interpret unseen unicode characters. |
| Outcome: | The proposed model improves number recognition by 89% over a CRF with a CNN and shows that unicode characters can be translated to unicoding. |
A Novel Approach to Part Name Discovery in Noisy Text (N18-3)
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| Challenge: | Part names are often multi-word terms longer than two words, and there is little consistency in how terms are described in noisy free text. |
| Approach: | They propose an algorithm that exploits statistical, linguistic and machine learning techniques to discover part names in noisy text. |
| Outcome: | The proposed method outperforms existing methods significantly in part name extraction. |
Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms (2024.lrec-main)
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| Challenge: | Semi-supervised learning that leverages synthetic data for training has been widely adopted for developing automatic post-editing models due to the lack of training data. |
| Approach: | They propose a method that uses masked tokens to generate a noisy text from a clean text by infilling mangled tokens with erroneous tokens. |
| Outcome: | The proposed method mimics translation errors found in real data and generates a noisy text from a clean text by infilling masked tokens with erroneous tokens. |
MTNT: A Testbed for Machine Translation of Noisy Text (D18-1)
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| Challenge: | Noisy input text can cause disastrous mistranslations in most modern machine translation systems. |
| Approach: | They propose a benchmark dataset for Machine Translation of Noisy Text (MTNT) they use reddit comments and professionally sourced translations to examine noise types. |
| Outcome: | The proposed dataset can provide an attractive testbed for noise-robust machine translation systems. |
Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power (D19-55)
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| Challenge: | Existing metrics to quantify lexical diversity have been proposed. |
| Approach: | They propose to examine how generic language characteristics are impacted by text alterations. |
| Outcome: | The proposed models show that lexical features are more sensitive to text modifications than syntactic ones. |
WebIE: Faithful and Robust Information Extraction on the Web (2023.acl-long)
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| Challenge: | Existing closed IE datasets are built using Wikipedia, but they have limitations when applied to web domains. |
| Approach: | They propose to annotate 25K triples from WebIE through crowdsourcing and introduce mWebIE, a translation of the annotated set in four other languages. |
| Outcome: | The proposed model trains on 1.6M sentences from the English Common Crawl corpus and includes negative examples to better reflect the data on the web. |
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning (2020.lrec-1)
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| Challenge: | Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the language. |
| Approach: | They propose to evaluate multiple cross-lingual word embedding models and compare their strengths and limitations to evaluate their effectiveness. |
| Outcome: | The proposed models perform well with noisy text and language pairs with major differences. |
Multimodal Robustness for Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing approaches to deal with noisy multimodal inputs are not robust enough to deal effectively with noisy data. |
| Approach: | They propose a method that composes domain adapters to deal with noisy inputs . they combine these adapters at runtime via dynamic routing or when source of noise is unknown . |
| Outcome: | The proposed model is flexible and state-of-the-art to deal with noisy multimodal inputs. |
Sentiment Analysis of Tweets using Heterogeneous Multi-layer Network Representation and Embedding (2020.emnlp-main)
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| Challenge: | Existing methods to handle sentiment analysis of tweets are inadequate due to various characteristics such as under-specificity, noise, and multilingual content. |
| Approach: | They propose a multi-layer network-based representation of tweets to generate multiple representations of a tweet and classify them using a neural-based early fusion approach. |
| Outcome: | The proposed method can address the problem of under-specificity, noisy text, and multilingual content present in a tweet and provides better representations than the text-based counterparts. |
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning (2023.emnlp-main)
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| Challenge: | Existing passive mental health monitoring systems use alternative features such as activity, app usage, and location via smartphones due to data privacy concerns. |
| Approach: | They propose a mobile mental health monitoring system that utilizes continuous speech and keyboard input in a privacy-preserving way via federated learning. |
| Outcome: | The proposed system achieves 0.15 AUROC improvement and 8.21% MAE reduction in self-reported depression, stress, anxiety, and mood from 46 participants. |
BERTwich: Extending BERT’s Capabilities to Model Dialectal and Noisy Text (2023.findings-emnlp)
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| Challenge: | Pre-trained language models like BERT deteriorate in the face of dialect variation or noise. |
| Approach: | They propose to sandwich BERT's encoder stack between additional encoder layers trained to perform masked language modeling on noisy text. |
| Outcome: | The proposed approach promotes zero-shot transfer to dialectal text and reduces embedding space between words and noisy counterparts. |