Papers by Judith Gaspers
Exploring Cross-Lingual Transfer Learning with Unsupervised Machine Translation (2021.findings-acl)
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| Challenge: | a new CLTL model is proposed to facilitate cross-linguistic transfer learning between distant languages . a key to CLTL is to learn a shared representation space for the given source-target language pair. |
| Approach: | They propose a new CLTL model that integrates machine translation with MT . they use an unannotated data technique to make use of the model's pre-training and fine-tuning . |
| Outcome: | The proposed model achieves better CLTL performance than the baseline model without more annotated data. |
MASSIVE-Agents: A Benchmark for Multilingual Function-Calling in 52 Languages (2025.findings-emnlp)
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| Challenge: | Using the original dataset, we cleaned up the MASSIVE dataset and reformatted it for evaluation within the Berkeley Function-Calling Leaderboard framework. |
| Approach: | They present a new benchmark for assessing multilingual function calling across 52 languages . they clean the original MASSIVE dataset and reformat it for evaluation . |
| Outcome: | The new benchmark covers 55 functions and 286 arguments in 52 languages. |
Sharing Encoder Representations across Languages, Domains and Tasks in Large-Scale Spoken Language Understanding (2023.acl-industry)
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Jonathan Hueser, Judith Gaspers, Thomas Gueudre, Chandana Prakash, Jin Cao, Daniil Sorokin, Quynh Do, Nicolas Anastassacos, Tobias Falke, Turan Gojayev
| Challenge: | Larger encoders can improve accuracy for spoken language understanding (SLU) but are difficult to use given the inference latency constraints of online systems. |
| Approach: | They propose to use a larger 170M parameter BERT encoder that shares representations across languages, domains and tasks for SLU. |
| Outcome: | The proposed encoders achieve state-of-the-art performance on numerous NLP tasks. |
Distributionally Robust Finetuning BERT for Covariate Drift in Spoken Language Understanding (2022.acl-long)
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| Challenge: | Covariate drift can occur when there is a drift between training and testing regarding what users request or how they request it. |
| Approach: | They propose a method that exploits natural variations in data to create a covariate drift in spoken language understanding datasets. |
| Outcome: | The proposed method improves robustness against covariate drift in spoken language understanding (SLU) it shows that a state-of-the-art model suffers performance loss under this drift. |
Towards Need-Based Spoken Language Understanding Model Updates: What Have We Learned? (2022.emnlp-industry)
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| Challenge: | In productionized machine learning systems, online model performance deteriorates when there is a distributional drift between offline training and online data. |
| Approach: | They propose a need-based retraining strategy guided by an efficient drift detector . they propose overlapping model releases, observation limitation and lack of annotated resources at runtime . |
| Outcome: | The proposed strategy reduces the cost of retraining models at fixed intervals . the proposed strategy can detect drifts when the model is applied on a new data set . |
Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System (N18-3)
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| Challenge: | In recent years, there has been growing interest in voice-controlled devices, such as Amazon Alexa or Google home. |
| Approach: | They investigate the use of Machine Translation to bootstrap a natural language understanding system for a new language for the use case of a large-scale voice-controlled device. |
| Outcome: | The proposed method reduces the time and cost of getting annotated corpus for a new language while still providing a large enough coverage of user requests. |
Cross-lingual Transfer Learning for Japanese Named Entity Recognition (N19-2)
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| Challenge: | a recent study focuses on bootstrapping named entity models from English to Japanese . TL is a technique that overcomes linguistic differences between the target and source languages . |
| Approach: | They propose to use a deep neural network model to transfer weights between languages . they also propose a novel approach that romanizes a portion of the Japanese input . |
| Outcome: | The proposed approach overcomes linguistic differences by romanizing a portion of the Japanese input. |
To What Degree Can Language Borders Be Blurred In BERT-based Multilingual Spoken Language Understanding? (2020.coling-main)
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| Challenge: | Existing models for multilingual SLU are mostly DNN-based joint models of intent classification and slot filling. |
| Approach: | They propose a BERT-based adversarial model architecture to learn language-shared and language-specific representations for multilingual SLU. |
| Outcome: | The proposed model narrows the gap to the ideal multilingual performance. |
Cross-lingual Transfer Learning with Data Selection for Large-Scale Spoken Language Understanding (D19-1)
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| Challenge: | Existing approaches to improve cross-lingual transfer learning on spoken language are pre-train on all available supervised data from another language. |
| Approach: | They propose a language model based source-language data selection method for cross-lingual transfer learning in spoken language understanding. |
| Outcome: | The proposed method reduces training time and improves model performance on spoken language understanding. |
Temporal Generalization for Spoken Language Understanding (2022.naacl-industry)
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| Challenge: | Spoken Language Understanding models are usually trained offline on historical data, but must perform well on incoming user requests after deployment. |
| Approach: | They propose different strategies for achieving good temporal generalization . they focus on temporal drift, where the distribution of utterances may change . |
| Outcome: | The proposed model can perform well on unseen domains, e.g., upcoming data. |