Papers with machine learning method
Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues (D19-50)
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| Challenge: | a blurry line between fake news and protected-speech satire has been a struggle for social media platforms . purveyors of fake news have begun to masquerade as satirical sites to avoid being demoted . |
| Approach: | They propose to automatically classify fake news versus satire based on language differences . they hypothesize that nuances could be identified using semantic and linguistic cues . |
| Outcome: | The proposed method can identify nuances between fake news and satire based on language differences . the proposed method is compared to the language-based baseline and is highly scalable . |
Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues (2020.lrec-1)
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| Challenge: | Using the MPAA rating, movie content can negatively affect children’s behaviour, for example, watching specific programs may encourage irresponsible sexual behavior and alcohol usage in teenagers. |
| Approach: | They propose an RNN-based architecture that jointly models the genre and the emotions in the script to predict the MPAA rating. |
| Outcome: | The proposed model outperforms the traditional machine learning method by 7% and achieves an 81% weighted F1 score. |
Detecting Lexical Borrowings from Dominant Languages in Multilingual Wordlists (2023.eacl-main)
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| Challenge: | Language contact is reflected in the transfer of words from donor to recipient languages. |
| Approach: | They propose to use two classical sequence comparison methods and one machine learning method to detect lexical borrowings in contact situations where dominant languages play an important role. |
| Outcome: | The proposed methods outperform classical methods on a sample of seven Latin American languages. |
Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation (N19-1)
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| Challenge: | Neural Machine Translation (NMT) performs poorly without large training corpora. |
| Approach: | They propose a machine learning method that retains the majority of general-domain performance lost in continued training without degrading in-domain. |
| Outcome: | The proposed method retains the majority of general-domain performance lost in continued training without degrading in-domain performances. |
Distribution of Emotional Reactions to News Articles in Twitter (L18-1)
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| Challenge: | Social networks have created datasets of opinions of users that focus on the writers' perspective, which does not consider the source that provokes those opinions. |
| Approach: | They propose to analyze opinions of Twitter users' after reading a news article and use it to predict the distribution of emotions. |
| Outcome: | The proposed dataset aims to explore how the six emotions are expressed by Twitter users' after reading a news article. |
JDCFC: A Japanese Dialogue Corpus with Feature Changes (L18-1)
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| Challenge: | Existing corpora focus on emotional expressions in conversations, but there are no large-scale corpors focusing on the relationships between emotions and utterances. |
| Approach: | They propose a Japanese Feature Change Knowledge Base (JFCKB) that focuses on emotional expressions in conversations. |
| Outcome: | The proposed corpus can recognize reasonableness of a given conversation. |
This Reads Like That: Deep Learning for Interpretable Natural Language Processing (2023.emnlp-main)
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| Challenge: | In this work, we explore the extension of prototypical networks to natural language processing. |
| Approach: | They propose a weighted similarity measure that enhances the similarity computation by focusing on informative dimensions of pre-trained sentence embeddings. |
| Outcome: | The proposed method improves predictive performance on AG News and RT Polarity datasets and the rationale-based recurrent convolutions. |