Papers by Jingjing Gong

5 papers
Convolutional Interaction Network for Natural Language Inference (D18-1)

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Challenge: Attention-based neural models have achieved great success in natural language inference (NLI).
Approach: They propose a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI.
Outcome: The proposed model can capture complex interactions on three large datasets.
Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging (D18-1)

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Challenge: Existing POS tagging methods for Twitter use labeled newswire text . however, Twitter users tend to mimic formal media expressions and develop linguistically informal styles.
Approach: They propose to use newswire text to learn POS tagging for Twitter while twitter users are developing linguistically informal styles.
Outcome: The proposed method achieves better performance than state-of-the-art methods on three different datasets.
Long Chain-of-Thought Fine-tuning via Understanding-to-Reasoning Transition (2025.emnlp-main)

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Challenge: Existing research on long-context scaling in language models has focused on managing lengthy input prompts instead of producing long outputs.
Approach: They propose a sequence-level curriculum learning framework that shifts a model’s focus from interpreting long chain-of-thoughts to generating them.
Outcome: Experiments on rigorous reasoning benchmarks, including AIME24 and GPQA Diamond, show that the proposed approach surpasses standard fine-tuning by over 10% while maintaining robust performance on understanding tasks.
Information Aggregation via Dynamic Routing for Sequence Encoding (C18-1)

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Challenge: Currently, little attention is paid to how to aggregate text sequences into fixed-size vectors.
Approach: They propose an aggregation mechanism to obtain a fixed-size encoding with a dynamic routing policy.
Outcome: The proposed method outperforms other aggregating methods on five text classification tasks.
EviReport: From Reasoned Outlines to Evidence Tracked Long-Form Reports (2026.findings-acl)

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Challenge: Evidence-intensive reports often produce fluent but under-supported drafts . eviReport is an evidence-grounded workflow for automated long-form report generation .
Approach: They propose an evidence-tracked workflow that organizes corpus evidence into compact, traceable units and retrieves query-relevant subgraphs into retrieval-ready packages.
Outcome: The proposed workflow outperforms baselines in factual coverage, factual accuracy and visual evidence integration.

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