Papers with statistical

4 papers
Fantastic Expressions and Where to Find Them: Chinese Simile Generation with Multiple Constraints (2023.acl-long)

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Challenge: Existing attempts to generate similes as context-free tasks are not suitable for simile generation . however, simile generated under such settings might be undesirable, we argue .
Approach: They propose a model to generate a simile with multiple simile elements . they propose to use a vehicle retrieval module to obtain the explicable comparison .
Outcome: The proposed model can generate a simile with multiple simile elements, e.g., context and vehicle.
Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results (2021.emnlp-main)

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Challenge: Existing approaches focus on high-level description of how research is carried out . instead, we focus on the subtleties of how experimental associations are presented .
Approach: They propose a transformer-based approach to relational scientific information extraction that captures associations over experimental variables and their qualifications, subtypes, and evidence.
Outcome: The proposed schema captures causal, comparative, predictive, statistical, and proportional associations over experimental variables along with qualifications, subtypes, and evidence.
Development of Automatic Speech Recognition for the Documentation of Cook Islands Māori (2022.lrec-1)

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Challenge: a new study describes the process of data processing and training of an automatic speech recognition system for Cook Islands Mori . the system is based on statistical and Deep Learning techniques, and is available under a license .
Approach: They describe the process of data processing and training of an automatic speech recognition system for Cook Islands Mori . they transcribed four hours of speech from adults and elderly speakers of the language and prepared two experiments .
Outcome: The proposed system can perform better with low-resource Indigenous languages . the system can be used to accelerate the documentation of Cook Islands Mori .
How do autoregressive transformers solve full addition? (2025.emnlp-main)

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Challenge: Large pre-trained language models have demonstrated impressive capabilities, but there is still much to learn about how they operate.
Approach: They investigate the ability of the autoregressive transformer to perform basic addition operations by using causal analysis to find that a few different attention heads in the middle layers control the addition carry . they found that due to the lack of global focus on the sequence within these attention heads, the model struggles to handle long-sequence addition tasks.
Outcome: The model performs basic addition tasks, but it still faces challenges with length generalization.

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