Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: Student Research Workshop
Emotional Intensity Estimation based on Writer’s Personality (2022.aacl-srw)
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| Challenge: | Existing emotion analysis models are difficult to accurately estimate the writer’s subjective emotions behind the text. |
| Approach: | They propose a method for personalized emotional intensity estimation based on a writer's personality test for Japanese SNS posts. |
| Outcome: | The proposed method improves on the existing method and the proposed hybrid model achieved state-of-the-art performance. |
Bipartite-play Dialogue Collection for Practical Automatic Evaluation of Dialogue Systems (2022.aacl-srw)
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| Challenge: | Existing methods for dialogue system evaluation are inefficient and time-consuming. |
| Approach: | They propose a dialogue collection method for automating dialogue system evaluation using bipartite-play method . authors propose constructing a better automatic evaluation method which is reproducible and low cost . |
| Outcome: | The proposed method correlates strongly with human subjectivity and human evaluation. |
Toward Building a Language Model for Understanding Temporal Commonsense (2022.aacl-srw)
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| Challenge: | Pre-trained language models such as BERT are still poor in temporal reasoning . commonsense reasoning is crucial for natural language processing (NLP) |
| Approach: | They propose to use multi-step fine-tuning and masked language modeling to predict mangled temporal indicators that are crucial for commonsense reasoning. |
| Outcome: | The proposed model improves performance on multiple time-related tasks. |
Optimal Summaries for Enabling a Smooth Handover in Chat-Oriented Dialogue (2022.aacl-srw)
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| Challenge: | In dialogue systems, it is difficult to provide fully autonomous dialogue . to ensure a good dialogue experience, human operators sometimes need to intervene . |
| Approach: | They conducted large-scale experiments on chat dialogues to determine which type of summary is most useful for handover . abstractive summary plus one utterance immediately before handover and extractive summary consisting of five utterrances immediately before the handover were found to be the most useful . |
| Outcome: | The best summaries were abstractive summary plus one utterance before handover and extractive summary consisting of five utterrances before hand over. |
MUTE: A Multimodal Dataset for Detecting Hateful Memes (2022.aacl-srw)
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| Challenge: | social media has enabled information propagation at unprecedented rate, but also generated malign content, such as hateful memes . a multimodal hate speech dataset is used to study the impact of hateful content on society . current studies focus on monolingual memes, but existing models cannot provide accurate inferences based on code-mixed captions a study on Bengali memes shows that joint evaluation of visual and textual features significantly improves the hateful data classification . |
| Approach: | They propose to use a multimodal hate speech dataset to detect hateful memes . they use monolingual captions in English and Bengali to analyze the content . |
| Outcome: | The proposed dataset shows that evaluation of visual and textual features significantly improves the hateful memes classification compared to unimodal evaluation. |
A Simple and Fast Strategy for Handling Rare Words in Neural Machine Translation (2022.aacl-srw)
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| Challenge: | Neural Machine Translation (NMT) has been gaining popularity due to its ability to bias in highfrequency words, low-frequency words have little chance of being considered in the inference process. |
| Approach: | They propose a strategy for integrating constraints during the training and decoding process to improve the translation of rare words. |
| Outcome: | The proposed approach improves translation of rare words in high and low-resource translation tasks, showing improvements of up to +1.8 BLEU scores over baseline systems. |
C3PO: A Lightweight Copying Mechanism for Translating Pseudocode to Code (2022.aacl-srw)
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| Challenge: | Existing low-code translators that translate pseudocode to code are expensive in terms of data and compute. |
| Approach: | They propose a lightweight alternative that exploits the property of code wherein most tokens can be simply copied from the pseudocode. |
| Outcome: | The proposed model reduces the computational cost and vocabulary sizes while reducing the computational costs and complexity. |
Outlier-Aware Training for Improving Group Accuracy Disparities (2022.aacl-srw)
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| Challenge: | Methods addressing spurious correlations such as Just Train Twice involve reweighting a subset of the training set to maximize the worst-group accuracy. |
| Approach: | They propose to reweight a subset of a training set to maximize the worst-group accuracy by detecting outliers and removing them before reweighing. |
| Outcome: | The proposed method achieves competitive or better accuracy compared with JTT and can detect and remove annotation errors in the subset being reweighted in JTT. |
An Empirical Study on Topic Preservation in Multi-Document Summarization (2022.aacl-srw)
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| Challenge: | Multi-document summarization is a process of generating an informative and concise summary from multiple topic-related documents. |
| Approach: | They perform empirical analysis on two MDS datasets and study topic preservation on generated summaries from 8 MDS models. |
| Outcome: | The results show that extractive and abstractive summarization methods preserve topic information from source documents. |
Detecting Urgency in Multilingual Medical SMS in Kenya (2022.aacl-srw)
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| Challenge: | Access to mobile phones has increased exponentially over the last 20 years, providing an opportunity to connect patients with healthcare interventions through mobile phones. |
| Approach: | They propose to use natural language processing to improve nurses' management of messages from pregnant and postpartum women in Kenya. |
| Outcome: | The proposed model did not reach the clinical usefulness threshold but could improve nurse workflow and responsiveness to urgent messages. |
Language over Labels: Contrastive Language Supervision Exceeds Purely Label-Supervised Classification Performance on Chest X-Rays (2022.aacl-srw)
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| Challenge: | Pretrained CLIP models lack domain-specific knowledge of text and images. |
| Approach: | They adapt CLIP-based models to the chest radiography domain using contrastive language supervision and a detailed ablation study of the batch and dataset size. |
| Outcome: | The proposed model outperforms supervised learning on labels on the MIMIC-CXR dataset while generalizing to the CheXpert and RSNA Pneumonia datasets. |
Dynamic Topic Modeling by Clustering Embeddings from Pretrained Language Models: A Research Proposal (2022.aacl-srw)
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| Challenge: | Neural Topic Models (NTMs) are topic models that are created with the help of a pretrained language model. |
| Approach: | They propose to do Neural Topic Modeling by Clustering document Embeddings (NTM-CE) with a pretrained language model to create dynamic topic models. |
| Outcome: | The proposed model can be evaluated theoretically and practically using quantitative measurements of coherence and human evaluation to evaluate the model. |
Concreteness vs. Abstractness: A Selectional Preference Perspective (2022.aacl-srw)
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| Challenge: | Using a collection of 5,438 nouns and 1,275 verbs, we exploit selectional preferences as a salient characteristic in classifying abstract vs. concrete words. |
| Approach: | They propose to use selectional preferences as a criterion to distinguish between concrete and abstract concepts and words. |
| Outcome: | The proposed method achieves an f1-score of 0.84 for nouns and 0.71 for verbs in classification and Spearman’s correlation of 0.86 for nonoms and 0.59% for verb. |