Papers by Konstantine Arkoudas
Low-Resource Compositional Semantic Parsing with Concept Pretraining (2023.eacl-main)
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| Challenge: | Semantic parsing is a key role in voice assistants by mapping natural language to structured meaning representations. |
| Approach: | They propose an architecture to perform domain adaptation automatically with only a small amount of metadata about the new domain and without any new training data. |
| Outcome: | The proposed architecture outperforms existing models in low-resource settings. |
Combining Weakly Supervised ML Techniques for Low-Resource NLU (2021.naacl-industry)
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| Challenge: | Recent advances in transfer learning have improved the performance of virtual assistants . however, meager training data is often a key bottleneck in creating voice-enabled applications . |
| Approach: | They propose to use unsupervised and semi-supervised techniques to improve NLU accuracy . they incorporate anonymized, unlabeled and automatically transcribed user utterances into training . |
| Outcome: | The proposed methods improve NLU accuracy in low-resource settings by integrating unsupervised and SSL techniques. |
Compositional Task-Oriented Parsing as Abstractive Question Answering (2022.naacl-main)
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| Challenge: | Task-oriented parsing (TOP) aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm. |
| Approach: | They propose to reduce TOP to abstractive question answering by using canonical paraphrasing to generate linearized parse trees. |
| Outcome: | The proposed technique outperforms state-of-the-art methods in full-data settings while achieving dramatic improvements in few-shot settings. |
Delexicalized Paraphrase Generation (2020.coling-industry)
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| Challenge: | Using convolutional neural networks, we generate delexicalized sentences . 1.29% accuracy is achieved with the generated paraphrases . |
| Approach: | They propose a neural paraphrasing model that generates delexicalized sentences . they use convolutional neural networks to pool on slot values and use pointers to locate them . |
| Outcome: | The proposed model generates delexicalized sentences with high quality . it can be used for intent classification and named entity recognition tasks . |
Stress-Testing the Reasoning Competence of Language Models With Formal Proofs (2025.findings-emnlp)
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| Challenge: | a new battery of challenging but tractable logical inference tasks is developed on ProofGrid . the tasks include proof writing and proof checking across propositional and equational logic . |
| Approach: | They propose a new battery of challenging but tractable logical inference tasks on ProofGrid . they introduce two new tasks: proof inpainting and proof gap-filling . |
| Outcome: | The proposed model performs well on top-tier models but also shows systematic failure modes. |