Papers by Guokan Shang
Abstractive Meeting Summarization: A Survey (2023.tacl-1)
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| Challenge: | Recent advances in deep learning have improved language generation systems, opening the door to improved forms of abstractive summarization. |
| Approach: | They propose to use neural encoder-decoder architectures to generate abstractive meeting summarizations that are particularly well-suited for multi-party conversation. |
| Outcome: | The proposed system could be used in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls. |
GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greek (2026.findings-acl)
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Yang Zhang, Mersin Konomi, Christos Xypolopoulos, Konstantinos Divriotis, Konstantinos Skianis, Giannis Nikolentzos, Giorgos Stamou, Guokan Shang, Michalis Vazirgiannis
| Challenge: | Existing evaluation benchmarks for large language models are limited for Greek . Existing datasets are often machine-translated from English, failing to capture Greek linguistic and cultural characteristics. |
| Approach: | They propose a native-sourced benchmark for massive multitask language understanding in Greek . they publicize 16,857 samples and reserve 4,948 samples for a private leaderboard . |
| Outcome: | The proposed model is based on 21,805 multiple-choice questions across 45 subject areas . the model is publicly released and reserved for a private leaderboard . |
DATScore: Evaluating Translation with Data Augmented Translations (2023.findings-eacl)
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| Challenge: | Experimental results show that DATScore correlates better with human meta-evaluations than the other recent state-of-the-art metrics. |
| Approach: | They propose to use data augmented translations to improve the evaluation of machine translations by using two new scoring strategies. |
| Outcome: | The proposed metric improves on 3 NLG tasks other than translation. |
The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text (2024.findings-naacl)
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| Challenge: | a new study examines the effects of training language models on synthetic data generated by their predecessors. |
| Approach: | They propose to use recursive finetuning techniques to assess linguistic diversity of models. |
| Outcome: | The proposed metrics show a decrease in diversity of model outputs through successive iterations, especially for tasks demanding high levels of creativity. |
Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning (2026.eacl-long)
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| Challenge: | Curriculum learning has improved efficiency across machine learning domains, but remains underexplored for language model pretraining. |
| Approach: | They present a systematic investigation of curriculum learning in LLM pretraining . they use vanilla curriculum learning, pacing-based sampling, and interleaved curricula . |
| Outcome: | The proposed framework accelerates convergence in early and mid-training phases, reducing training steps by 18-45% to reach baseline performance. |
Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text (2026.acl-long)
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| Challenge: | Code-switching (CSW) is widespread in multilingual communities and increasingly prevalent in online content. |
| Approach: | They propose a pipeline for producing linguistically grounded CSW variants of established benchmarks across five typologically diverse languages. |
| Outcome: | The proposed model sets show that inserting non-English tokens into English reduces accuracy on comprehension and reasoning benchmarks, whereas embedding English into non- English contexts often improves it. |
FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation (2022.acl-long)
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| Challenge: | Existing evaluation metrics are not reliable, but require significant computational resources. |
| Approach: | They propose a method to learn a fixed, low cost version of any expensive NLG metric while retaining most of its original performance. |
| Outcome: | The proposed approach retains most of the original performance while running faster and faster. |
MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Models (2026.acl-demo)
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| Challenge: | MixtureKit is a modular open-source framework for constructing, training, and analyzing Mixture-of-Experts (MoE) models from arbitrary pre-trained or fine-tuned checkpoints. |
| Approach: | They propose a modular open-source framework for constructing, training, and analyzing Mixture-of-Experts (MoE) models from arbitrary pre-trained or fine-tuned checkpoints. |
| Outcome: | Experiments on multilingual code-switched (Arabic–Latin) show that BTX models built with MixtureKit outperform dense baselines across multiple benchmarks. |
FREDSum: A Dialogue Summarization Corpus for French Political Debates (2023.findings-emnlp)
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| Challenge: | Recent advances in deep learning have improved the performance of abstractive summarization systems. |
| Approach: | They present a dataset of french political debates to enhance resources for multi-lingual dialogue summarization. |
| Outcome: | The proposed dataset will be made publicly available for use by the research community. |
Speaker-change Aware CRF for Dialogue Act Classification (2020.coling-main)
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| Challenge: | Recent work in Dialogue Act (DA) classification approaches the task as a sequence labeling problem, using neural network models coupled with a Conditional Random Field (CRF) as the last layer. |
| Approach: | They propose to modify the CRF layer to take speaker-change into account and learn meaningful transition patterns conditioned on speaker-changing DA labels. |
| Outcome: | The proposed model outperforms the original model with wide margins for some DA labels. |
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization (P18-1)
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Guokan Shang, Wensi Ding, Zekun Zhang, Antoine Tixier, Polykarpos Meladianos, Michalis Vazirgiannis, Jean-Pierre Lorré
| Challenge: | a novel graph-based framework for abstractive meeting speech summarization is developed . instead of grammatical, well-segmented sentences, the input is made of often ill-formed and ungrammatically ungrammatized text fragments called utterances. |
| Approach: | They propose a graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on annotations. |
| Outcome: | The proposed framework improves on the state-of-the-art on the AMI and ICSI corpus. |
LLM as a Broken Telephone: Iterative Generation Distorts Information (2025.acl-long)
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| Challenge: | Large language models are increasingly responsible for online content, but they can be distorted by repeated transmission. |
| Approach: | They investigate whether large language models distort information through iterative generation. |
| Outcome: | The findings raise important questions about the reliability of LLM-generated content in iterative workflows. |
Fast-Decoding Diffusion Language Models via Progress-Aware Confidence Schedules (2026.findings-acl)
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| Challenge: | *SchED* is a training-free, model-agnostic early-exit algorithm that terminates diffusion decoding using a progress-aware confidence threshold. |
| Approach: | They propose a training-free, model-agnostic early-exit algorithm that terminates diffusion decoding using a progress-aware confidence threshold. |
| Outcome: | The proposed algorithm achieves 4 speedups on instruction-tuned models while maintaining baseline performance on average. |
Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding (2020.aacl-main)
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| Challenge: | Abstractive community detection is an important spoken language understanding task, whose goal is to group utterances according to whether they can be jointly summarized by a common abstractive sentence. |
| Approach: | They propose a neural contextual utterance encoder with three types of self-attention mechanisms and train it using the siamese and triplet energy-based meta-architectures. |
| Outcome: | The proposed system outperforms multiple energy-based and non-energy based baselines on the AMI corpus. |
Automatic Analysis of Substantiation in Scientific Peer Reviews (2023.findings-emnlp)
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| Challenge: | Existing systems to analyze peer reviews' quality are inadequate due to the increasing workload of reviewers and the lack of domain experts . |
| Approach: | They propose to use a claim-evidence pair extraction problem to analyze substantiation in peer reviews and train an argument mining system to do the same. |
| Outcome: | The proposed system could be used by conference managers and reviewers to analyze the quality of peer reviews. |