| Challenge: | Recent studies have shown that EEG signal magnitude and topography depend on word length, frequency and open vs. closed class. |
| Approach: | They propose to use EEG to predict Part-of-Speech (PoS) tags from neural signals measured at millisecond resolution during text reading. |
| Outcome: | The proposed techniques outperform linear-SVMs on PoS tagging of unigram and bigram data. |
Similar Papers
Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text (L18-1)
Copied to clipboard
Géraldine Damnati, Jeremy Auguste, Alexis Nasr, Delphine Charlet, Johannes Heinecke, Frédéric Béchet
| Challenge: | a new approach to POS tagging noisy user generated text is proposed . word embeddings are trained on a noisy corpus to address both normalization and POS. |
| Approach: | They propose to use word embeddings to normalize text before tagging it, while a gated neural network based tagger handles the remaining errors. |
| Outcome: | The proposed approach normalizes some errors before tagging, while a gated neural network handles the remaining errors. |
Encoding and Decoding Language in the Brain with Language Models (2026.eacl-tutorials)
Copied to clipboard
| Challenge: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and brain-based fine-caching with language models. |
| Approach: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and scaling with language models. |
| Outcome: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and decoding with language models. |
Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging? (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent Part-Of-Speech (POS) induction models assume certain independence assumptions that do not hold in real languages. |
| Approach: | They propose a Masked Part-of-Speech Model (MPoSM) that can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction. |
| Outcome: | The proposed model can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction. |
Rethinking Cross-Subject Data Splitting for Brain-to-Text Decoding (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have successfully decoded natural language from non-invasive brain signals . current dataset splitting methods suffer from data leakage problem . |
| Approach: | They propose a right cross-subject data splitting criterion without data leakage for decoding fMRI and EEG signal to text. |
| Outcome: | The proposed method overfits and overestimates brain-to-text decoding models. |
Domain adaptation for part-of-speech tagging of noisy user-generated text (N19-1)
Copied to clipboard
| Challenge: | Existing POS taggers for canonical German text achieve good results around 97% accuracy, but when applying these trained models to out-of-domain data the performance decreases drastically. |
| Approach: | They propose a neural network that trains an out-of-domain model on a large newswire corpus and transfers those weights by using them as a prior for a model trained on the target domain. |
| Outcome: | The proposed model achieves a tagging accuracy of slightly over 90%, improving on the previous state of the art for this task. |
Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing (2023.tacl-1)
Copied to clipboard
| Challenge: | Sequence-to-Sequence (S2S) models have been successful on text generation tasks . however, learning complex structures with S2S models remains challenging . |
| Approach: | They propose to use constrained decoding to model part-of-speech tagging, named entity recognition, constituency, and dependency parsing tasks with 3 lexically diverse linearization schemas and corresponding constrained coding methods. |
| Outcome: | The proposed methods outperform the state-of-the-art on four core tasks. |
Smart-Start Decoding for Neural Machine Translation (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing neural machine translation models adopt a monotonic decoding order of either left-to-right or right-to left. |
| Approach: | They propose a method that starts decoding target words from the right side of a median word and generates words on the left. |
| Outcome: | The proposed method outperforms baseline models on three datasets. |
Relating Simple Sentence Representations in Deep Neural Networks and the Brain (P19-1)
Copied to clipboard
| Challenge: | Existing deep learning models for natural language processing are not fully studied. |
| Approach: | They investigate whether deep recurrent models learn sentences against those encoded by the brain and whether there is any correspondence between hidden layers of these models and brain regions when processing sentences. |
| Outcome: | The proposed models can be used to synthesize brain data and improve subsequent stimuli decoding accuracy. |
End-to-end ASR to jointly predict transcriptions and linguistic annotations (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing models generate audio transcripts by sequentially producing likely graphemes, or multi-graphemic units, from which lexical items of a language can be recovered. |
| Approach: | They propose a Transformer-based sequence-to-sequence model for automatic speech recognition that can produce high-quality transcriptions and linguistic annotations. |
| Outcome: | The proposed model can produce high-quality transcriptions and linguistic annotations on Japanese and English audio datasets. |
Cross-Register Projection for Headline Part of Speech Tagging (2021.emnlp-main)
Copied to clipboard
| Challenge: | Part of speech (POS) tagging models are underperforming on headlines due to differences in the register of English news headlines and long-form text. |
| Approach: | They propose to annotate news headlines with POS tags by projecting predicted tags from corresponding sentences in news bodies. |
| Outcome: | The proposed model reduces errors by 23% and 19% on a newly-annotated corpus of over 5,248 English news headlines from the Google sentence compression corpus. |