Papers with contextual models
Data-Efficient Methods For Improving Hate Speech Detection (2023.findings-eacl)
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| Challenge: | Existing methods for hate speech detection are data-hungry and require large datasets. |
| Approach: | They propose an input-level data augmentation technique EasyMix to improve hate speech detection in english and multilingual datasets. |
| Outcome: | The proposed method improves the performance across english and multilingual datasets by 1% and 2-8%. |
„Mann“ is to “Donna” as「国王」is to « Reine » Adapting the Analogy Task for Multilingual and Contextual Embeddings (2023.starsem-1)
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| Challenge: | a lack of comparable multilingual benchmarks and a consensual evaluation protocol for contextual models remains an open question. |
| Approach: | They propose a multilingual analogy dataset and evaluate human and contextual embedding performance. |
| Outcome: | The proposed dataset evaluates human and contextual embedding models on the analogy task. |
The (Undesired) Attenuation of Human Biases by Multilinguality (2022.emnlp-main)
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| Challenge: | odor pleasantness perception is universal, but cultural biases are not always present in embedding models . et al., 2018: a new study shows that cultural bias is not always the case in embedded models based on human texts . |
| Approach: | They propose multilingual cultural aware tests to quantify biases in embedding models . they find that biased models are more likely to be multilingual than monolingual ones . |
| Outcome: | The results show that human preferences are not always universal . they also show that multilinguality reverses biases, despite differences in training corpus . |
Integrating Multimodal Information in Large Pretrained Transformers (2020.acl-main)
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Wasifur Rahman, Md Kamrul Hasan, Sangwu Lee, AmirAli Bagher Zadeh, Chengfeng Mao, Louis-Philippe Morency, Ehsan Hoque
| Challenge: | Recent Transformer-based contextual word representations have shown state-of-the-art performance in multiple disciplines within NLP. |
| Approach: | They propose an attachment to BERT and XLNet that allows them to accept multimodal nonverbal data during fine-tuning. |
| Outcome: | The proposed attachment allows BERT and XLNet to accept multimodal nonverbal data during fine-tuning. |
ChatEL: Entity Linking with Chatbots (2024.lrec-main)
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| Challenge: | Entity Linking (EL) is a challenging task in natural language processing . existing approaches focus on creating elaborate contextual models that are unwieldy and difficult to train . |
| Approach: | They propose a framework to prompt LLMs to return accurate results for Entity Linking . they use a three-step framework to generate a set of EL models that can be open-source . |
| Outcome: | The proposed framework improves the average F1 performance across 10 datasets by more than 2%. |
Building Static Embeddings from Contextual Ones: Is It Useful for Building Distributional Thesauri? (2022.lrec-1)
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| Challenge: | contextual language models are dominant in the field of Natural Language Processing, but they are not suitable for all uses. |
| Approach: | They propose a method for building word or type-level embeddings from contextual models . they evaluate a large set of English nouns from the perspective of extracting semantic similarity relations . |
| Outcome: | The proposed method can be used to build word or type embeddings from contextual models . it can be exploited for a wide set of English nouns, showing it can improve distributional thesauri . |
Is anisotropy really the cause of BERT embeddings not being semantic? (2022.findings-emnlp)
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| Challenge: | Existing approaches to train contextual language models for NLP use a lightweight approach called bi-encoder, which takes two sentences as input, but does not perform well with vanilla pre-trained Transformers. |
| Approach: | They conduct a set of experiments to improve our understanding of the lack of semantic isometry in contextualized word representations in BERT. |
| Outcome: | The proposed approach does not perform well with vanilla pre-trained Transformers. |
Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding (N19-1)
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| Challenge: | Existing models that use contextual information of dialogues to improve spoken language understanding (SLU) select the wrong history when the histories are similar in content. |
| Approach: | They propose time-aware models that automatically learn the latent time-decay function of the history without a manual time- decay. |
| Outcome: | The proposed models achieve higher F1 scores than state-of-the-art models on a benchmark dataset . |
Obtaining Better Static Word Embeddings Using Contextual Embedding Models (2021.acl-long)
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| Challenge: | Recent contextual word embeddings have prohibitively high computational cost in many use-cases and are hard to interpret. |
| Approach: | They propose a distillation method which is an extension of CBOW-based training and improves computational efficiency of NLP applications. |
| Outcome: | The proposed method outperforms existing models and existing models in terms of quality and performance. |
Embeddings models for Buddhist Sanskrit (2022.lrec-1)
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| Challenge: | Despite extensive scholarly endeavors, much uncertainty still surrounds this body of literature, especially regarding matters of chronology, authorship, compositional history. |
| Approach: | They propose a corpus of Buddhist texts, a general corpus and word similarity and word analogy datasets for embeddings models. |
| Outcome: | The proposed models perform better on semantic similarity and word analogy tasks than on contextual models. |
A Large-Scale Japanese Dataset for Aspect-based Sentiment Analysis (2022.lrec-1)
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| Challenge: | Aspect-based sentiment analysis (ABSA) has not been explored in the Japanese language . there is no standard Japanese dataset available for ABSA task in the language - a paper by cnn. |
| Approach: | They propose to use a Japanese aspect-based sentiment analysis dataset for hotel reviews domain . they propose to include 53,192 review sentences with seven aspect categories and two polarity labels . |
| Outcome: | The proposed dataset contains 53,192 review sentences with seven aspect categories and two polarity labels. |