Papers by Suzanna Sia
CLIReval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task (2020.acl-demos)
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| Challenge: | evaluating machine translation (MT) with cross-lingual information retrieval is relatively time-consuming and subjective. |
| Approach: | They propose a toolkit that evaluates machine translation with a proxy task of cross-lingual information retrieval. |
| Outcome: | The proposed toolkit is based on the "metrics shared task" of WMT2019. |
Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates (2022.emnlp-main)
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| Challenge: | Existing work on persuasion in online forums focuses on identifying debate winners and winning negotiation games. |
| Approach: | They adopt a hierarchical generative Variational Autoencoder model to model winning arguments . they propose competing hypotheses about the nature of argumentation . |
| Outcome: | The proposed model predicts winning arguments in reddit debates . it uses a hierarchical generative Variational Autoencoder to model argumentation . |
Adaptive Mixed Component LDA for Low Resource Topic Modeling (2021.eacl-main)
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| Challenge: | Probabilistic topic models in low resource settings are faced with less reliable estimates due to sparsity of discrete word co-occurrence counts. |
| Approach: | They propose a mixture model which interpolates between discrete and continuous topic-word distributions and utilises pre-trained embeddings to improve topic coherence. |
| Outcome: | The proposed model outperforms fully discrete, fully continuous, and static mixture models on topic coherence in low resource settings. |
Anti-LM Decoding for Zero-shot In-context Machine Translation (2024.findings-naacl)
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| Challenge: | Existing approaches to zero-shot learning with large language models are poorly calibrated for zero-shoot tasks. |
| Approach: | They propose a contrastive decoding objective with a decay factor to address in-context bias . they conduct experiments on 3 model types and sizes, 3 language directions, and beam search . |
| Outcome: | The proposed method outperforms state-of-the-art decoding objectives with 20 BLEU points improvement from the default objective in some settings. |
Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too! (2020.emnlp-main)
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| Challenge: | Existing topic models rely on probabilistic models to uncover themes within document collections, but are they the only option? |
| Approach: | They propose a way to cluster pre-trained word embeddings while incorporating document information for weighted clustering and reranking top words. |
| Outcome: | The proposed approach performs as well as classical topic models, but with lower runtime and computational complexity. |