Search Results for author:
Found 37 papers, 7 papers with code
Towards Automatic Short Answer Assessment for Finnish as a Paraphrase Retrieval Task
Automatic grouping of textual answers has the potential of allowing batch grading, but is challenging because the answers, especially longer essays, have many claims.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Out-of-Domain Evaluation of Finnish Dependency Parsing
The prevailing practice in the academia is to evaluate the model performance on in-domain evaluation data typically set aside from the training corpus.
Semantic Search as Extractive Paraphrase Span Detection
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 identify its paraphrase in a given document, the same modelling setup as typically used in extractive question answering.
Annotation Guidelines for the Turku Paraphrase Corpus
This document describes the annotation guidelines used to construct the Turku Paraphrase Corpus.
Quantitative Evaluation of Alternative Translations in a Corpus of Highly Dissimilar Finnish Paraphrases
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 non-trivial variation in translation.
Finnish Paraphrase Corpus
Out of all paraphrase pairs in our corpus 98% are manually classified to be paraphrases at least in their given context, if not in all contexts.
Towards Fully Bilingual Deep Language Modeling
Language models based on deep neural networks have facilitated great advances in natural language processing and understanding tasks in recent years.
Turku Enhanced Parser Pipeline: From Raw Text to Enhanced Graphs in the IWPT 2020 Shared Task
We present the approach of the TurkuNLP group to the IWPT 2020 shared task on Multilingual Parsing into Enhanced Universal Dependencies.
WikiBERT models: deep transfer learning for many languages
In this paper, we introduce a simple, fully automated pipeline for creating language-specific BERT models from Wikipedia data and introduce 42 new such models, most for languages up to now lacking dedicated deep neural language models.
The FISKM\"O Project: Resources and Tools for Finnish-Swedish Machine Translation and Cross-Linguistic Research
This paper presents FISKM{\"O}, a project that focuses on the development of resources and tools for cross-linguistic research and machine translation between Finnish and Swedish.
Multilingual is not enough: BERT for Finnish
Deep learning-based language models pretrained on large unannotated text corpora have been demonstrated to allow efficient transfer learning for natural language processing, with recent approaches such as the transformer-based BERT model advancing the state of the art across a variety of tasks.
Morphological Tagging and Lemmatization of Albanian: A Manually Annotated Corpus and Neural Models
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 lemmatizer trained on it.
Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task
We present the approach taken by the TurkuNLP group in the CRAFT Structural Annotation task, a shared task on dependency parsing.
Is Multilingual BERT Fluent in Language Generation?
The multilingual BERT model is trained on 104 languages and meant to serve as a universal language model and tool for encoding sentences.
Template-free Data-to-Text Generation of Finnish Sports News
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, interpretation, insight into the game, and quotes that are not present in the official statistics.
Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks
We evaluate our lemmatizer on 52 different languages and 76 different treebanks, showing that our system outperforms all latest baseline systems.
Mind the Gap: Data Enrichment in Dependency Parsing of Elliptical Constructions
In this paper, we focus on parsing rare and non-trivial constructions, in particular ellipsis.
Enhancing Universal Dependency Treebanks: A Case Study
We evaluate two cross-lingual techniques for adding enhanced dependencies to existing treebanks in Universal Dependencies.
Turku Neural Parser Pipeline: An End-to-End System for the CoNLL 2018 Shared Task
In this paper we describe the TurkuNLP entry at the CoNLL 2018 Shared Task on Multilingual Parsing from Raw Text to Universal Dependencies.
Ranked #5 on
Dependency Parsing
on Universal Dependencies
Cross-Lingual Pronoun Prediction with Deep Recurrent Neural Networks v2.0
In this paper we present our system in the DiscoMT 2017 Shared Task on Crosslingual Pronoun Prediction.
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
TurkuNLP: Delexicalized Pre-training of Word Embeddings for Dependency Parsing
We present the TurkuNLP entry in the CoNLL 2017 Shared Task on Multilingual Parsing from Raw Text to Universal Dependencies.
