120 citations · 223 across the 19 of their papers we have counts for
21 papers
Your Prompt Should Do More: Effects of Retrieval Instructions in Embedding Models
Amanda Myntti, Jenna Kanerva, Veronika Laippala +1
Prompted embedding models have recently received increasing attention, particularly for retrieval, where detailed retrieval instructions are provided as part of the retrieval promp…
MultiSynt/MT: Trillion-Token Multi-Parallel Pre-Training Data Translated Across 36 Languages
Maximilian Idahl, Jörg Tiedemann, Sampo Pyysalo +19
Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synthetic parallel corpus with approxi…
Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance
Amanda Myntti, Jenna Kanerva, Veronika Laippala +1
In this paper, we show that high-performing embedding models organize their embedding spaces in a consistent way. We evaluate 25 contemporary embedding models on five MTEB tasks sp…
Measuring Social Integration Through Participation: Categorizing Organizations and Leisure Activities in the Displaced Karelians Interview Archive using LLMs
Joonatan Laato, Veera Schroderus, Jenna Kanerva +3
Digitized historical archives make it possible to study everyday social life on a large scale, but the information extracted directly from text often does not directly allow one to…
Creating a Historical Migration Dataset from Finnish Church Records, 1800-1920
Ari Vesalainen, Jenna Kanerva, Aida Nitsch +4
This article presents a large-scale effort to create a structured dataset of internal migration in Finland between 1800 and 1920 using digitized church moving records. These record…
Extracting Social Connections from Finnish Karelian Refugee Interviews Using LLMs
Joonatan Laato, Jenna Kanerva, John Loehr +2
We performed a zero-shot information extraction study on a historical collection of 89,339 brief Finnish-language interviews of refugee families relocated post-WWII from Finnish Ea…
OCR Error Post-Correction with LLMs in Historical Documents: No Free Lunches
Jenna Kanerva, Cassandra Ledins, Siiri Käpyaho +1
Optical Character Recognition (OCR) systems often introduce errors when transcribing historical documents, leaving room for post-correction to improve text quality. This study eval…
FinGPT: Large Generative Models for a Small Language
Risto Luukkonen, Ville Komulainen, Jouni Luoma +18
Large language models (LLMs) excel in many tasks in NLP and beyond, but most open models have very limited coverage of smaller languages and LLM work tends to focus on languages wh…
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran +74
Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using…
Out-of-Domain Evaluation of Finnish Dependency Parsing
Jenna Kanerva, Filip Ginter
The prevailing practice in the academia is to evaluate the model performance on in-domain evaluation data typically set aside from the training corpus. However, in many real world…
Semantic Search as Extractive Paraphrase Span Detection
Jenna Kanerva, Hanna Kitti, Li-Hsin Chang +3
In this paper, we approach the problem of semantic search by framing the search task as paraphrase span detection, i.e. given a segment of text as a query phrase, the task is to id…
Annotation Guidelines for the Turku Paraphrase Corpus
Jenna Kanerva, Filip Ginter, Li-Hsin Chang +8
This document describes the annotation guidelines used to construct the Turku Paraphrase Corpus. These guidelines were developed together with the corpus annotation, revising and e…
Quantitative Evaluation of Alternative Translations in a Corpus of Highly Dissimilar Finnish Paraphrases
Li-Hsin Chang, Sampo Pyysalo, Jenna Kanerva +1
In this paper, we present a quantitative evaluation of differences between alternative translations in a large recently released Finnish paraphrase corpus focusing in particular on…
Finnish Paraphrase Corpus
Jenna Kanerva, Filip Ginter, Li-Hsin Chang +7
In this paper, we introduce the first fully manually annotated paraphrase corpus for Finnish containing 53,572 paraphrase pairs harvested from alternative subtitles and news headin…
Towards Fully Bilingual Deep Language Modeling
Li-Hsin Chang, Sampo Pyysalo, Jenna Kanerva +1
Language models based on deep neural networks have facilitated great advances in natural language processing and understanding tasks in recent years. While models covering a large…
WikiBERT models: deep transfer learning for many languages
Sampo Pyysalo, Jenna Kanerva, Antti Virtanen +1
Deep neural language models such as BERT have enabled substantial recent advances in many natural language processing tasks. Due to the effort and computational cost involved in th…
Multilingual is not enough: BERT for Finnish
Antti Virtanen, Jenna Kanerva, Rami Ilo +5
Deep learning-based language models pretrained on large unannotated text corpora have been demonstrated to allow efficient transfer learning for natural language processing, with r…
Morphological Tagging and Lemmatization of Albanian: A Manually Annotated Corpus and Neural Models
Nelda Kote, Marenglen Biba, Jenna Kanerva +2
In this paper, we present the first publicly available part-of-speech and morphologically tagged corpus for the Albanian language, as well as a neural morphological tagger and lemm…
Is Multilingual BERT Fluent in Language Generation?
Samuel Rönnqvist, Jenna Kanerva, Tapio Salakoski +1
The multilingual BERT model is trained on 104 languages and meant to serve as a universal language model and tool for encoding sentences. We explore how well the model performs on…
Template-free Data-to-Text Generation of Finnish Sports News
Jenna Kanerva, Samuel Rönnqvist, Riina Kekki +2
News articles such as sports game reports are often thought to closely follow the underlying game statistics, but in practice they contain a notable amount of background knowledge,…
Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks
Jenna Kanerva, Filip Ginter, Tapio Salakoski
In this paper we present a novel lemmatization method based on a sequence-to-sequence neural network architecture and morphosyntactic context representation. In the proposed method…