Papers with F-scores
A k-Nearest Neighbor Approach towards Multi-level Sequence Labeling (N19-2)
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| Challenge: | Existing methods for complex dialog management require limited training data. |
| Approach: | They propose a method for intent recognition for complex dialog management in low resource situations . they use windowed word n-grams, POS tag n grams and pre-trained word embeddings as features . |
| Outcome: | The proposed method performs better with less than 1% of the data size than existing methods but requires considerably more data. |
Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering (D19-57)
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| Challenge: | Existing systems for coreference resolution are difficult because of their long coreferent chains. |
| Approach: | They propose to use an existing span-based neural coreference resolution system as a baseline . they filter noisy mentions based on parse trees and integrate a highly expressive language model into the system . |
| Outcome: | The proposed system outperforms the baseline system on the CRAFT Shared Tasks 2019 task. |
Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling (N19-1)
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| Challenge: | Existing work on opinion role labeling (ORL) is highly correlative with semantic role labeled (SRL) SRL is used to identify opinion holders and holder expressions for a given predicate. |
| Approach: | They propose a method to enhance opinion role labeling by presenting semantic-aware word representations which are learned from SRL. |
| Outcome: | The proposed method outperforms two other methods on a benchmark MPQA corpus and achieves higher F scores. |
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding (C18-1)
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| Challenge: | Existing work which augments an utterance without considering its relation with other utterrances, however, has failed to improve the language understanding module. |
| Approach: | They propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance’s same semantic alternatives in the training data. |
| Outcome: | The proposed framework achieves 6.38 and 10.04 F-scores on the Airline Travel Information System dataset and a newly created semantic frame annotation on the Stanford Multi-turn, Multi-domain Dialogue Dataset. |
CVAE-based Re-anchoring for Implicit Discourse Relation Classification (2021.findings-emnlp)
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| Challenge: | Existing studies show that training implicit discourse relation classifiers suffers from data sparsity. |
| Approach: | They propose a re-anchoring strategy to reduce the risk of erroneous sampling . they use Conditional VAE to estimate the risk and migrate the anchor to reduce it . |
| Outcome: | The proposed method improves the baseline classifier performance on PDTB v2.0 . |
A Deep Neural Network based Approach for Entity Extraction in Code-Mixed Indian Social Media Text (L18-1)
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| Challenge: | a huge number of people use social media to express and exchange information in their own languages. |
| Approach: | They propose to use a code-mixed environment to extract higher level features from text . they use 'gadget' algorithm that automatically discovers higher level feature from text. |
| Outcome: | The proposed approach is generic and does not make use of handcrafted features or rules. |
Is it worth it? Budget-related evaluation metrics for model selection (L18-1)
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| Challenge: | linguistic resources can be labor-intensive, requiring great amounts of work-hours and expert annotation. |
| Approach: | They propose a machine learning model that pre-annotates or filters content before annotating it . they argue that the model with the highest F-score may not have best separation . |
| Outcome: | a case study shows that the model with the highest F-score does not yield the highest profits . the model that has the highest score does not produce the highest profit, the study shows . |
An analysis of language models for metaphor recognition (2020.coling-main)
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| Challenge: | Metaphor recognition systems that are based on language models perform substantially worse on unconventional metaphors than on conventional ones. |
| Approach: | They conduct a linguistic analysis of recent metaphor recognition systems based on language models and a variant of BERT language models to examine their performance. |
| Outcome: | The proposed systems show that they can recognise unseen words if synonyms or morphological variations have been seen before, leading to enhanced generalisation beyond word sense disambiguation. |
Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations (2022.coling-1)
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| Challenge: | Existing semantic parsers are based on deep learning, but rule-based approaches offer advantages . a drawback of neural semantic parses is that their output lacks explainability . |
| Approach: | They propose a method that maps a syntactic dependency tree to a formal meaning representation using a series of graph transformations. |
| Outcome: | The proposed method outperforms neural parsers in English, German, Italian and Dutch. |
Augmenting word2vec with latent Dirichlet allocation within a clinical application (N19-1)
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| Challenge: | Existing models that combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions are not suitable for clinical binary text classification tasks. |
| Approach: | They propose three models that combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions. |
| Outcome: | The proposed models outperform word2vec and LDA models on a clinical binary text classification task. |
An Annotated Social Media Corpus for German (2020.lrec-1)
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| Challenge: | Hate Speech (HS) against ethnic, religious and national minorities is a growing concern in online discourse. |
| Approach: | They present the German Twitter section of a large (2 billion word) bilingual Social Media corpus for Hate Speech research. |
| Outcome: | The proposed parser achieved F-scores of 97% for morphology and 92% for syntax on a cross-section of tweets. |