Papers by Judit Ács
Better Together: Modern Methods Plus Traditional Thinking in NP Alignment (2020.lrec-1)
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| Challenge: | a recent study shows that end-to-end systems are not structurally free. |
| Approach: | They propose to use dictionary- and word vector-based baselines to align NPs in the bitext . they argue that alignment of NP's in MT can be improved by using old-fashioned methods . |
| Outcome: | a new study shows that alignment of NPs in the bitext is relevant even in an end-to-end paradigm . the proposed system can be improved by bringing in old-fashioned methods, the authors argue . |
BME-UW at SRST-2019: Surface realization with Interpreted Regular Tree Grammars (D19-63)
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| Challenge: | adaamko's system restores word order and inflection from a graph of typed, directed dependencies between lemmas. |
| Approach: | They propose a method that restores word order and inflection from a graph of typed, directed dependencies between lemmas. |
| Outcome: | The proposed system restores word order and inflection from a graph of typed, directed dependencies between lemmas. |
From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization (2024.lrec-main)
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| Challenge: | Existing models and datasets for training summarization models are limited for less resourceful languages like Hungarian . |
| Approach: | They propose to use a Hungarian corpus for training abstractive and extractive summarization models by cleaning, preprocessing and deduplication. |
| Outcome: | The proposed model trains abstractive and extractive summarization models using the dataset . it will be made publicly available, encouraging replication, further research, and real-world applications across various domains. |
Subword Pooling Makes a Difference (2021.eacl-main)
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| Challenge: | Contextual word-representations use subword tokenization to handle large vocabularies and unknown words. |
| Approach: | They propose to use the first subword for morphological probing, POS tagging and NER to pool multiple subwords that correspond to a single word in contextual language models. |
| Outcome: | The proposed model outperforms two multilingual models on morphological probing, POS tagging and NER tasks in 9 languages. |