Constituency Parsing

Latest papers

10 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Multi-Task Learning by using Contextualized Word Representations for Syntactic Parsing of a Morphologically Rich Language
Sep 24, 2026Toqeer Ehsan, Miriam Butt, Sarmad Hussain +2Multi-Task Learning Low-Resource Language Processing
Reliable Intelligence Team, VTT Technical Research Centre of Finland Ltd., 02150 Espoo, Finland. · Department of Linguistics, University of Konstanz, 78464 Konstanz, Germany. · Center for Language Engineering, Al-Khawarizmi Institute of Computer Science, University of Engineering and Technology, 54890 Lahore, Pakistan. +2
DPH Parser: A Bottom-Up Grammar-Driven Parser for Joint Constituency and Dependency Analysis
Sep 5, 2026Hussein GhalyConstituency Parsing Dependency Parsing
Independent Researcher New York, New York
Representing and Parsing Korean Constituency Structure at Different Levels of Granularity
Aug 27, 2026Jungyeul Park, KyungTae Lim, Zihao Huang +3Constituency Parsing
Korea Advanced Institute of Science & Technology, South Korea · The University of British Columbia, Canada · The Chinese University of Hong Kong, Hong Kong +1
Non-binary bottom-up constituency parsing without arity actions
Jul 12, 2026Jungyeul Park, Eunkyul Leah Jo, Zihao HuangConstituency Parsing
Korea Advanced Institute of Science & Technology, Daejeon, South Korea · The University of British Columbia, Vancouver, Canada
Holographic Neural PCFG for Unsupervised Parsing
Jul 9, 2026Ryosuke Yamaki, Daichi Mochihashi, Nobutaka Shimada +1Constituency Parsing
Ritsumeikan University, Japan · The Institute of Statistical Mathematics, Japan · Kyoto University, Japan
Better heads do not guarantee better binarized constituency parsing
May 27, 2026Zeyao Qi, Yige Chen, Eitan Klinger +2Constituency Parsing
The Chinese University of Hong Kong, Ma Liu Shui, Hong Kong · The University of British Columbia, Vancouver, Canada · Korea Advanced Institute of Science & Technology, Daejeon, South Korea
Exploiting Pre-trained Encoder-Decoder Transformers for Sequence-to-Sequence Constituent Parsing
May 13, 2026Daniel Fernández-González, Cristina Outeiriño CidTransformer Constituency Parsing
Universidade de Vigo, Departamento de Informática, Campus As Lagoas s/n, Ourense, 32004,, Spain
Learning Constituent Headedness
Mar 16, 2026Zeyao Qi, Yige Chen, KyungTae Lim +2Constituency Parsing Structured Prediction
The Chinese University of Hong Kong · Korea Advanced Institute of Science & Technology
What Exactly do Children Receive in Language Acquisition? A Case Study on CHILDES with Automated Detection of Filler-Gap Dependencies
Mar 2, 2026Zhenghao Herbert Zhou, William Dai, Maya Viswanathan +3Constituency Parsing Child Language Acquisition
Department of Linguistics, Yale University · Department of Computer Science, Yale University · Wu Tsai Institute, Yale University
Constituency Structure over Eojeol in Korean Treebanks
Dec 27, 2025Jungyeul Park, Chulwoo ParkConstituency Parsing
KAIST, South Korea. · Anyang University, South Korea.

Sep 24, 2026J/K move · Enter open · S save

Multi-Task Learning by using Contextualized Word Representations for Syntactic Parsing of a Morphologically Rich Language

Toqeer Ehsan, Miriam Butt, Sarmad Hussain, Hassan Alhuzali+1

Reliable Intelligence Team, VTT Technical Research Centre of Finland Ltd., 02150 Espoo, Finland. · Department of Linguistics, University of Konstanz, 78464 Konstanz, Germany. · Center for Language Engineering, Al-Khawarizmi Institute of Computer Science, University of Engineering and Technology, 54890 Lahore, Pakistan.+2

Multi-Task Learning Low-Resource Language Processing Dependency Parsing Constituency Parsing

Open paper AskPDF arXiv

We address the challenge of syntactic parsing for Urdu, a morphologically rich language, and present state-of-the-art results for both constituency and dependency parsing. This paper offers four major contributions: 1) the conversion of the CLE-UTB phrase structure treebank into a dependency treebank by developing language-specific head-word and phrase-to-dependency label mapping rules; 2) a novel sequence labeling scheme that transforms the parsing task into a unified representation; 3) the training of contextualized word representations on a large 220 million tokens Urdu corpus collected from the web; and 4) development of parsing framework using two learning paradigms, single-task and multi-task learning. Several post-processing rules are applied to improve the quality of the automatically converted dependency structure treebank. The proposed sequence labeling scheme enables the use of a shared architecture that learns the syntactic structures from both grammatical structures simultaneously and hence improves generalization. Experiments show that the multi-task learning setup significantly enhances parsing performance, achieving an F1 score of 91.39 for constituency parsing (an improvement of 3.29 points) and a labeled attachment score of 85.69 for dependency parsing (an improvement of 1.49 points). These results demonstrate that learning cross-task representations provides measurable benefits and advances the state of syntactic parsing for Urdu.