Papers by Philippe Langlais
Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine Translation (C18-1)
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| Challenge: | Parallel sentence extraction is a task addressing the data sparsity problem found in multilingual natural language processing applications. |
| Approach: | They propose a bidirectional recurrent neural network based approach to extract parallel sentences from multilingual corpora. |
| Outcome: | The proposed approach outperforms existing approaches on noisy parallel corpora and shows significant improvements in translation performance. |
Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition (2021.tacl-1)
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| Challenge: | Name Regularity Bias is a problem in NER models that use contextual information to predict the type of an ambiguous entity. |
| Approach: | They propose a model-agnostic training method that adds learnable adversarial noise to some entity mentions to improve their accuracy. |
| Outcome: | The proposed method outperforms feature-based models on name regularity bias . it adds learnable adversarial noise to some entity mentions, leading to gains . |
Effective Data Augmentation for Sentence Classification Using One VAE per Class (2022.coling-1)
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| Challenge: | Variational auto-encoders and its conditional variant the Conditional-VAE (CVAE) are often used to generate new textual data, but they require more complex manipulations to ensure that the generated examples are useful. |
| Approach: | They propose a simple way to use Variational Auto-Encoders (VAE) for data augmentation by training one VAE per class. |
| Outcome: | The proposed method outperforms generative models on binary classification tasks and several dataset sizes on four different tasks. |
RW-KD: Sample-wise Loss Terms Re-Weighting for Knowledge Distillation (2021.findings-emnlp)
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| Challenge: | Knowledge Distillation (KD) is used to compress the pre-training and task-specific fine-tuning phases of large neural language models. |
| Approach: | They propose a sample-wise loss weighting method that re-weights the two losses for each sample. |
| Outcome: | The proposed method outperforms existing methods on 7 datasets of the GLUE benchmark. |
CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems (2024.findings-acl)
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Abbas Ghaddar, David Alfonso-Hermelo, Philippe Langlais, Mehdi Rezagholizadeh, Boxing Chen, Prasanna Parthasarathi
| Challenge: | CHARP is a testbed for knowledge-grounded dialogue evaluation of models trained on FaithDial data. |
| Approach: | They propose a testbed for evaluating models trained on FaithDial with annotation artifacts that may bias models towards completely ignoring the conversation history. |
| Outcome: | The proposed model fails to accurately evaluate the conversational history and lacks hallucination detection. |
SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling (N19-1)
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| Challenge: | Multi-task learning (MTL) has been studied for sequence labeling tasks . auxiliary tasks are selected specifically to improve performance of a target task . |
| Approach: | They propose a shared-cell long-short-term memory cell which contains shared parameters that can learn from all tasks and task-specific parameters that could learn task-related information. |
| Outcome: | The proposed model can learn from all tasks and task-specific parameters. |
EUROPA: A Legal Multilingual Keyphrase Generation Dataset (2024.acl-long)
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| Challenge: | Keyphrases are short phrases that describe a text and have been used for many applications. |
| Approach: | They present a dataset for multilingual keyphrase generation in the legal domain . it is derived from legal judgments from the Court of Justice of the European Union . they run multilingual models on the corpus and analyze the results . |
| Outcome: | The proposed dataset shows that it is better than existing models and can capture larger input context. |
A Methodology for Building a Diachronic Dataset of Semantic Shifts and its Application to QC-FR-Diac-V1.0, a Free Reference for French (2022.lrec-1)
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| Challenge: | Existing algorithms to detect semantic shifts have been criticized for their difficulty in evaluating them. |
| Approach: | They propose a method for building a reference dataset for semantic shift detection . they use a word-sense disambiguation model to associate a date of first appearance to all senses of a term . |
| Outcome: | The proposed method is based on a word-sense disambiguation model . significant changes in sense distributions and stability are detected . the resulting words are inspected by experts using a dedicated interface . |
Revisiting the Task of Scoring Open IE Relations (L18-1)
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| Challenge: | Recent Open Information Extraction systems allow us to extract ever larger (yet incomplete) open-domain Knowledge Bases from text. |
| Approach: | They propose a baseline model which gives competitive results in a previously defined protocol and provides an independent source of signal to judge arbitrary fact plausibility. |
| Outcome: | The proposed model gives competitive results in the previously defined protocol and provides an independent source of signal to judge arbitrary fact plausibility. |
Unsupervised multiple-choice question generation for out-of-domain Q&A fine-tuning (2022.acl-short)
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| Challenge: | Pre-trained models have shown very good performances on a number of question answering benchmarks especially when fine-tuned on multiple question answering datasets at once. |
| Approach: | They propose an approach to fine-tune a question-answering dataset using a rule-based algorithm that generates questions and answers from unannotated sentences. |
| Outcome: | The proposed model can generate questions and answers from unannotated sentences on a multiple-choice physics, biology and chemistry benchmark. |
ReGLA: Refining Gated Linear Attention (2025.naacl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) are known for their computational and storage requirements due to the quadratic computation complexity of softmax attention. |
| Approach: | They propose to reduce the quadratic computation complexity of softmax attention by using feature maps, normalization and the gating mechanism to improve performance. |
| Outcome: | The proposed model outperforms existing gated linear attention models in extensive tasks including training from scratch and post-linearization with continual pre-training. |
Transforming Wikipedia into a Large-Scale Fine-Grained Entity Type Corpus (L18-1)
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| Challenge: | et al. (2017): WiFiNE annotated with fine-grained entity types . lack of a well-established training corpus makes it difficult to manually annotate the amount of data needed for training. |
| Approach: | They propose an English corpus annotated with fine-grained entity types based on Wikipedia . they use heuristics to build a large, high quality, annotating corpus using 2 manually annotized benchmarks . |
| Outcome: | The proposed system outperforms the existing systems with two datasets and gains a 2.8 macro F1 score. |
Is ChatGPT the ultimate Data Augmentation Algorithm? (2023.findings-emnlp)
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| Challenge: | Recent research has examined the use of ChatGPT for data augmentation, but only in limited contexts. |
| Approach: | They use ChatGPT to create new data with paraphrasing and zero-shot generation to compare it to seven other algorithms. |
| Outcome: | The proposed model performs exceptionally well on some simpler data, but it does not perform better than the other algorithms. |
RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation (2022.findings-naacl)
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Md Akmal Haidar, Nithin Anchuri, Mehdi Rezagholizadeh, Abbas Ghaddar, Philippe Langlais, Pascal Poupart
| Challenge: | Existing methods for intermediate layer knowledge distillation suffer from computational burdens and engineering efforts for setting up a proper layer mapping. |
| Approach: | They propose a method where intermediate layers from teacher and student models are randomly selected to be distilled into intermediate layers of student models. |
| Outcome: | The proposed method outperforms state-of-the-art intermediate layer knowledge distillation methods on GLUE tasks and out-of domain test sets. |
On Evaluation Protocols for Data Augmentation in a Limited Data Scenario (2025.coling-main)
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| Challenge: | Textual data augmentation (DA) is a prolific field of study where novel techniques to create artificial data are regularly proposed. |
| Approach: | They propose to use textual data augmentation (DA) to generate new sentences for text classification in a limited data setting. |
| Outcome: | The proposed methods perform better on small data settings and on large datasets, but they are not as effective on large data sets. |
A linguistically-motivated evaluation methodology for unraveling model’s abilities in reading comprehension tasks (2024.emnlp-main)
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| Challenge: | Existing models fail for linguistic characteristics of input examples, despite the impressive quantity of scientific studies dedicated to them, the capabilities, limitations, and risks of these models remain largely unknown. |
| Approach: | They propose to use semantic frame annotation to characterize examples by a small number of complexity factors to account for model’s difficulty. |
| Outcome: | The proposed evaluation methodology is based on the intuition that certain examples consistently yield lower scores regardless of model size or architecture. |
BenchIE^FL: A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark (2024.findings-acl)
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| Challenge: | Numerous OIE systems claim ever-increasing performance, limiting their effectiveness. |
| Approach: | They propose a benchmark which fully enforces the principles of BenchIE while containing fewer errors, omissions and shortcomings when candidate facts are matched towards reference ones. |
| Outcome: | The proposed benchmark aims to improve the performance of OIE extractors while avoiding errors and omissions. |
CHARPEVAL: Benchmarking Large Language Models’ Contextual Reasoning in Knowledge-Grounded Dialogue (2025.findings-acl)
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| Challenge: | Existing benchmarks that evaluate the ability of Large Language Models (LLMs) to perform contextualized reasoning in knowledge-grounded dialogue scenarios are lacking. |
| Approach: | They propose a benchmark to evaluate the ability of Large Language Models to perform contextualized reasoning in knowledge-grounded dialogue scenarios. |
| Outcome: | The proposed benchmark shows that open-weight LLMs are ineffective at reasoning over discontinuous chunks of text across the input. |