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Natural Language Engineering

Subject Areas: Artificial Intelligence
Publisher: Cambridge University Press - Cambridge University Press
ISSN: 1469-8110
SJR: 57

Open Access

Journal Article

Editorial Note

Mitkov, Ruslan

2024 Natural Language Engineering

doi:10.1017/S1351324924000020
Open Access

Journal Article

Quinductor: A multilingual data-driven method for generating reading-comprehension questions using Universal Dependencies

Kalpakchi, Dmytro; Boye, Johan

2024 Natural Language Engineering

doi:10.1017/S1351324923000037

Abstract We propose a multilingual data-driven method for generating reading comprehension questions using dependency trees. Our method provides a strong, deterministic and inexpensive-to-train baseline for less-resourced languages. While a language-specific corpus is still required, its size is...
Open Access

Journal Article

Determining sentiment views of verbal multiword expressions using linguistic features

Wiegand, Michael; Schulder, Marc; Ruppenhofer, Josef

2024 Natural Language Engineering

doi:10.1017/S1351324923000153

Abstract We examine the binary classification of sentiment views for verbal multiword expressions (MWEs). Sentiment views denote the perspective of the holder of some opinion. We distinguish between MWEs conveying the view of the speaker of the utterance (e.g., in “The company reinvented the...
Open Access

Journal Article

What should be encoded by position embedding for neural network language models?

Yu, Shuiyuan; Zhang, Zihao; Liu, Haitao

2024 Natural Language Engineering

doi:10.1017/S1351324923000128

Abstract Word order is one of the most important grammatical devices and the basis for language understanding. However, as one of the most popular NLP architectures, Transformer does not explicitly encode word order. A solution to this problem is to incorporate position information by means of...
Open Access

Journal Article

Towards diverse and contextually anchored paraphrase modeling: A dataset and baselines for Finnish

Kanerva, Jenna; Ginter, Filip; Chang, Li-Hsin; Rastas, Iiro; Skantsi, Valtteri; Kilpeläinen, Jemina; Kupari, Hanna-Mari; Piirto, Aurora; Saarni, Jenna; Sevón, Maija; Tarkka, Otto

2024 Natural Language Engineering

doi:10.1017/S1351324923000086

Abstract In this paper, we study natural language paraphrasing from both corpus creation and modeling points of view. We focus in particular on the methodology that allows the extraction of challenging examples of paraphrase pairs in their natural textual context, leading to a dataset...
Open Access

Journal Article

Urdu paraphrase detection: A novel DNN-based implementation using a semi-automatically generated corpus

Iqbal, Hafiz Rizwan; Maqsood, Rashad; Raza, Agha Ali; Hassan, Saeed-Ul

2024 Natural Language Engineering

doi:10.1017/S1351324923000189

Abstract Automatic paraphrase detection is the task of measuring the semantic overlap between two given texts. A major hurdle in the development and evaluation of paraphrase detection approaches, particularly for South Asian languages like Urdu, is the inadequacy of standard evaluation...
Open Access

Journal Article

Polish natural language inference and factivity: An expert-based dataset and benchmarks

Ziembicki, Daniel; Seweryn, Karolina; Wróblewska, Anna

2024 Natural Language Engineering

doi:10.1017/S1351324923000220

Abstract Despite recent breakthroughs in Machine Learning for Natural Language Processing, the Natural Language Inference (NLI) problems still constitute a challenge. To this purpose, we contribute a new dataset that focuses exclusively on the factivity phenomenon; however, our task remains the...
Open Access

Journal Article

Emerging trends: When can users trust GPT, and when should they intervene?

Church, Kenneth

2024 Natural Language Engineering

doi:10.1017/S1351324923000578

Abstract Usage of large language models and chat bots will almost surely continue to grow, since they are so easy to use, and so (incredibly) credible. I would be more comfortable with this reality if we encouraged more evaluations with humans-in-the-loop to come up with a better...

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