Papers with classification problems
Pairwise Representation Learning for Event Coreference (2022.starsem-1)
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| Challenge: | Existing work induces mention representations independently by extracting features from the sentence that contains the mention, without using the context of the other mention. |
| Approach: | They propose a Pairwise Representation Learning scheme for the event mention pairs that jointly encodes a pair of text snippets so that the representation of each mention in the pair is induced in the context of the other one. |
| Outcome: | The proposed scheme outperforms state-of-the-art representations on cross-document and within-document benchmarks. |
Complaint Analysis and Classification for Economic and Food Safety (D19-51)
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João Filgueiras, Luís Barbosa, Gil Rocha, Henrique Lopes Cardoso, Luís Paulo Reis, João Pedro Machado, Ana Maria Oliveira
| Challenge: | Governmental institutions are using artificial intelligence to deal with specific problems and exploit their huge amounts of structured and unstructured information. |
| Approach: | They propose to use natural language processing and machine learning to classify complaints . they use feature-based approaches and traditional classifiers to analyze complaints based on citizen feedback . |
| Outcome: | The proposed methods have accuracy scores above 70% and can be used to improve public services. |
Domain-Agnostic Neural Architecture for Class Incremental Continual Learning in Document Processing Platform (2023.acl-industry)
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| Challenge: | Recent methods with stochastic gradient learning struggle in streaming data setups and are restricted to specific domains. |
| Approach: | They propose a fully differentiable architecture that enables the training of high-performance classifiers when examples from each class are presented separately. |
| Outcome: | The proposed architecture achieves SOTA results without a memory buffer and clearly outperforms the reference methods. |
Linguistic Profiling of a Neural Language Model (2020.coling-main)
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| Challenge: | Neural Language Models (NLMs) have become a central component in NLP systems over the last few years, showing outstanding performance and improving the state-of-the-art on many tasks. |
| Approach: | They use a wide set of probing tasks to investigate how a Neural Language Model learns linguistic properties before and after a fine-tuning process. |
| Outcome: | The proposed model can encode a wide range of linguistic characteristics but loses this information when trained on specific downstream tasks. |
Event Extraction as Multi-turn Question Answering (2020.findings-emnlp)
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| Challenge: | Current approaches to event extraction fail to model rich interactions among event types and arguments of different roles. |
| Approach: | They propose a new paradigm that formulates event extraction as multi-turn question answering . they propose to use reading comprehension problems to extract triggers and arguments . |
| Outcome: | The proposed approach outperforms current state-of-the-art on argument extraction tasks . it makes full use of dependency among arguments and event types, and generalizes well . |
Adversarial Attack and Defense of Structured Prediction Models (2020.emnlp-main)
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| Challenge: | Existing approaches to building effective adversarial attackers focus on classification problems. |
| Approach: | They propose a framework that learns to attack a structured prediction model with feedbacks from multiple reference models. |
| Outcome: | The proposed framework is able to attack state-of-the-art models and boost them with training . it is based on a sequence-to-sequence model with feedbacks from multiple reference models . |
Text Classification with Few Examples using Controlled Generalization (N19-1)
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| Challenge: | Current training data for text classification is limited, resulting in limited generalization capacity. |
| Approach: | They propose a feed-forward network that can generalize from unlabeled parsed corpora to produce task-specific semantic vectors. |
| Outcome: | The proposed approach is especially effective in low-data scenarios compared to state-of-the-art methods. |
The Effects of Unimodal Representation Choices on Multimodal Learning (L18-1)
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| Challenge: | In the real world, multiple modes of information are gathered to create knowledge in a way humans can understand. |
| Approach: | They propose to combine unimodal representations to map multiple modes of information to a single space . they argue that the way they are combined can affect performance and classification metrics . |
| Outcome: | The proposed model can be used to correlate words in a textual description of an object with multimodal representations. |
Towards Integration of Statistical Hypothesis Tests into Deep Neural Networks (P19-1)
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| Challenge: | Existing approaches for text classification are lexicallevel features with Naive Bayes or Support Vector Machines (SVM) . |
| Approach: | They propose a deep-learning model that uses label descriptions to train texts and their labels for multi-label and multi-class classification tasks. |
| Outcome: | The proposed model improves on one set with a high margin and on all other sets with competitive results. |
Toward Human Readable Prompt Tuning: Kubrick’s The Shining is a good movie, and a good prompt too? (2023.findings-emnlp)
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| Challenge: | Large language models can perform downstream tasks in a zero-shot fashion, given natural language prompts that specify the desired behavior. |
| Approach: | They propose a human readable prompt tuning method that incorporates a fluency constraint to find a distribution of effective and fluent prompts. |
| Outcome: | The proposed method outperforms baselines by 7.0% across three tasks. |