Papers by Jingcheng Niu
Rationally Reappraising ATIS-based Dialogue Systems (P19-1)
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| Challenge: | Recent state-of-the-art neural models have obtained F1-scores near 98% on the task of slot filling. |
| Approach: | They propose to fix annotation errors in ATIS and propose a rule-based grammar for slot filling that achieves a 95.82% F1 score. |
| Outcome: | The proposed grammar achieves a 95.82% F1-score on the ATIS domain. |
Does BERT Rediscover a Classical NLP Pipeline? (2022.coling-1)
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| Challenge: | Existing theories of BERT's structure lack conclusive empirical support . however, there is scepticism about the premises of probing itself . |
| Approach: | They propose a new probe called GridLoc that can take into account token positions, training rounds, and random seeds. |
| Outcome: | The proposed probe detects other, stronger regularities suggesting appeals to layer depth may not be the preferable mode of explanation for BERT’s inner workings. |
Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning (2025.emnlp-main)
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| Challenge: | Existing methods such as LoRA and VeRA use memory-efficient methods to fine-tune large language models. |
| Approach: | They propose a method that uses only 1–5% of the standard LoRA parameters and achieves state-of-the-art performance across a wide range of tasks. |
| Outcome: | The proposed method achieves state-of-the-art performance across a wide range of tasks using only 1–5% of the standard LoRA parameters. |
Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support (2022.emnlp-industry)
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Stephen Obadinma, Faiza Khan Khattak, Shirley Wang, Tania Sidhorn, Elaine Lau, Sean Robertson, Jingcheng Niu, Winnie Au, Alif Munim, Karthik Raja Kalaiselvi Bhaskar
| Challenge: | Creating agent assistants that can help improve customer service support requires inputs from industry users and their customers as well as knowledge of state-of-the-art natural language processing (NLP) technology. |
| Approach: | They propose to combine expertise from academia and industry to build task/domain-specific Neural Agent Assistants with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. |
| Outcome: | The proposed framework is based on three case studies of industry partners who successfully adapt the framework to their unique challenges. |
Llama See, Llama Do: A Mechanistic Perspective on Contextual Entrainment and Distraction in LLMs (2025.acl-long)
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| Challenge: | *contextual entrainment* occurs across a wide range of language models (LMs) and prompt settings. |
| Approach: | They hypothesize that there is a circuit of attention heads that corresponds to the phenomenon *contextual entrainment* . when they "turn off" these heads, the effect of contextual entraining is significantly attenuated. |
| Outcome: | The proposed method shows that LMs assign higher logits to tokens that have previously appeared in the context prompt, even for random tokens. |
Temporal Histories of Epidemic Events (THEE): A Case Study in Temporal Annotation for Public Health (2020.lrec-1)
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| Challenge: | Current EBS estimates the occurrence time of events based on coarse metadata such as document publication time. |
| Approach: | They propose a temporal annotation standard THEE-TimeML and a corpus TheeBank . they document the corpus annotation process and demonstrate the immediate benefit . |
| Outcome: | The proposed standards are based on the existing timeML and the corpus TheeBank . the proposed standards demonstrate the immediate benefit to public health applications . |
ConTempo: A Unified Temporally Contrastive Framework for Temporal Relation Extraction (2024.findings-acl)
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| Challenge: | Temporal relation extraction (TRE) is a task of classifying temporal relations between events conveyed in narratives. |
| Approach: | They propose a Temporally Contrastive learning model that increases the model’s awareness of the meaning of temporal relations by leveraging their symmetric or antisymmetric properties. |
| Outcome: | The proposed model improves the model's representation of meaning of temporal relations and its ability to integrate with the underlying temporal calculus. |
Sheaf Discovery with Joint Computation Graph Pruning and Flexible Granularity (2025.emnlp-main)
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| Challenge: | Experimental results show that DiscoGP extracts sheaves that preserve 93-100% of a model’s performance while comprising only 1-7% of the original weights and connections. |
| Approach: | They propose a framework for extracting self-contained modular units within neural language models (LMs) they use a gradient-based pruning algorithm to prune the original LM to a sparse skeleton . |
| Outcome: | The proposed framework preserves 93-100% of the original model's performance while preserving only 1-7% of the model''s original weights and connections. |