Papers with PC

14 papers
Findings of the Association for Computational Linguistics: EACL 2024 (2024.findings-eacl)

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Challenge: EACL 2024 is the first conference to adopt ARR only . 2024 will be the year where we see whether it actually works .
Approach: cnn's anna mccartney is the general chair of the 18th conference of the European Chapter of the Association for Computational Linguistics . 2024 will be the year where we see whether it actually works .
Outcome: the 18th conference of the European Chapter of the Association for Computational Linguistics will be held in london . organizers decided to move the conference from a month-long conference to a year-long one .
Proceedings of the 28th International Conference on Computational Linguistics (2020.coling-main)

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Challenge: COLING2020 is the 28th International Conference on Computational Linguistics . the conference received a record 2195 submissions, surpassing the previous record of 2195 .
Approach: a record 2195 submissions were received for the conference, surpassing the previous record of 2195 . ICCL has a rich programme of over 653 papers, 7 tutorials, and 22 workshops .
Outcome: the 28th International Conference on Computational Linguistics is now a virtual one . the conference received a record 2195 submissions, surpassing the previous record of 2195 .
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers) (2024.eacl-short)

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Challenge: EACL 2024 is the first conference to adopt ARR only . 2024 will be the year where we see whether it actually works .
Approach: cnn's anna mccartney is the general chair of the 18th conference of the European Chapter of the Association for Computational Linguistics . 2024 will be the year where we see whether it actually works .
Outcome: the 18th conference of the European Chapter of the Association for Computational Linguistics will be held in london . organizers decided to move the conference from a month-long conference to a year-long one .
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (D18-1)

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Challenge: EMNLP 2018 received a record-breaking 2,231 valid submissions, a 48% increase over EMnLP 2017 . EMNA 2018 will have 14 workshops, 6 tutorials, 3 invited speakers, 351 long paper presentations, 198 short paper presentations and 10 TACL paper presentations .
Approach: EMNLP 2018 will have 14 workshops, 6 tutorials, 3 invited speakers, 351 long paper presentations, 198 short paper presentations and 10 TACL paper presentations.
Outcome: EMNLP 2018 received a record-breaking 2,231 valid submissions, a 48% increase over EMnLP 2017 . the program co-chairs put tremendous care into every decision, big and small, and handled numerous inquiries and requests .
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) (2024.eacl-long)

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Challenge: EACL 2024 is the first conference to adopt ARR only . 2024 will be the year where we see whether it actually works .
Approach: cnn's anna mccartney is the general chair of the 18th conference of the European Chapter of the Association for Computational Linguistics . 2024 will be the year where we see whether it actually works .
Outcome: the 18th conference of the European Chapter of the Association for Computational Linguistics will be held in london . organizers decided to move the conference from a month-long conference to a year-long one .
Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings (2025.acl-long)

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Challenge: Large language models excel at downstream NLP tasks through in-context learning . however, the internal mechanisms behind ICL remain under-explored .
Approach: They propose a PC patching approach to identify modules where input-label mappings function . they observe and verify that key heads utilize input-labeled mappings to generate target labels for new queries.
Outcome: The proposed approach detects modules where input-label mappings function . it also detects that key heads use the mappings to generate labels for new queries .
INSURE-Dial: A Phase-Aware Conversational Dataset Benchmark for Compliance Verification and Phase Detection (2026.eacl-long)

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Challenge: Administrative phone tasks drain roughly $1 trillion annually from healthcare, with over 500 million calls manually handled in 2024.
Approach: They propose a new benchmark for developing and assessing compliance-aware voice agents for phase-against-span-based call auditing with span-based compliance verification.
Outcome: The proposed framework is the first public benchmark for developing and assessing compliance-aware voice agents for phase-against-span-based call auditing with span-based compliance verification.
Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations (2024.findings-emnlp)

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Challenge: Existing CRSs can be highly persuasive, but they can be deceptive and can damage the long-term trust between users and the CRS.
Approach: They propose a method to enhance the credibility of CRS’s explanations by using a set of credibility-aware persuasive strategies and a post-hoc self-reflection process.
Outcome: The proposed method enhances the credibility of CRS’s explanations and refines them via post-hoc self-reflection.
PersonaForge: Psychology-Grounded Dual-Process Architecture for Personality-Consistent Role-Playing Agents (2026.findings-acl)

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Challenge: Existing approaches to role-playing with Large Language Models lack consistency across long conversations.
Approach: They propose a three-layer personality architecture grounded in psychological theory and a dual-process generation mechanism inspired by cognitive science to solve this problem.
Outcome: The proposed framework reduces drift over 50-turn conversations by reducing personality consistency . human evaluation confirms more authentic and psychologically coherent character behaviors.
Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation (2022.coling-1)

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Challenge: Existing approaches to train NMT models rely on sparse parallel data . a variety of PC variants yield significant improvements for low-resource NMT .
Approach: They propose to transfer well-trained NMT models to low-resource languages by bidirectionally-adaptive learning strategy . they divide inner constituents of Parent encoder into two "teams" aiming to adapt to characteristics of low- and high-resourced languages .
Outcome: The proposed method improves on low-resource NMT models with a variety of PC variants.
LPC: A Logits and Parameter Calibration Framework for Continual Learning (2022.findings-emnlp)

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Challenge: Existing approaches to solve catastrophic forgetting problem are varied . current approaches to learn continuous learning are based on replay-based methods .
Approach: They propose to calibrate parameters and logits so that preserving old parameters and generalized learning on new concepts can be solved simultaneously.
Outcome: The proposed model achieves state-of-the-art performance in all scenarios.
Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right (2021.emnlp-main)

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Challenge: Large language models have shown promising results in zero-shot settings due to surface form competition . since probability mass is finite, this lowers the probability of the correct answer .
Approach: They propose a scoring function that compensates for surface form competition by reweighing each option according to its a priori likelihood.
Outcome: The proposed scoring function achieves consistent gains in zero-shot over calibrated and uncalibrated scoring functions on all GPT-2 and GPT-3 models on a variety of multiple choice datasets.
Tracing Training Footprints: A Calibration Approach for Membership Inference Attacks Against Multimodal Large Language Models (2025.findings-emnlp)

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Challenge: Existing methods to improve difficulty calibration for Multimodal Large Language Models only consider text input . visual embeddings in training data reduce effectiveness of these methods .
Approach: They propose a method to detect member samples in poorly generalized local manifolds by visual embeddings.
Outcome: The proposed method surpasses existing methods.
Mind Reader: Latent User Demand-Guided Content Optimization for Generative Search Engine (2026.acl-long)

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Challenge: Generative Search Engines (GSEs) have reshaped information retrieval and Generating Engine Optimization (GEO) emerges to improve the content visibility in GSEs’ responses.
Approach: They propose a method to optimize content to cover latent semantic information of GSEs by decomposing query into diverse perspectives and capturing underlying semantic information.
Outcome: The proposed method outperforms baselines and effectively improves content visibility (with up to 2.44x objective metrics and 1.23x subjective metrics on average).

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