1 code implementation • NAACL (WOAH) 2022 • Shuzhou Yuan, Antonis Maronikolakis, Hinrich Schütze
Research to tackle hate speech plaguing online media has made strides in providing solutions, analyzing bias and curating data.
no code implementations • EMNLP 2021 • Timo Schick, Hinrich Schütze
Providing pretrained language models with simple task descriptions in natural language enables them to solve some tasks in a fully unsupervised fashion.
1 code implementation • EACL (AdaptNLP) 2021 • Antonis Maronikolakis, Hinrich Schütze
Thus, instead of training multiple models, we can train a single multidomain model saving on computational resources and training time.
no code implementations • LREC (BUCC) 2022 • Silvia Severini, Viktor Hangya, Masoud Jalili Sabet, Alexander Fraser, Hinrich Schütze
The two approaches we find most effective are: 1) using identical words as seed lexicons (which unsupervised approaches incorrectly assume are not available for orthographically distinct language pairs) and 2) combining such lexicons with pairs extracted by matching romanized versions of words with an edit distance threshold.
no code implementations • Findings (EMNLP) 2021 • Antonis Maronikolakis, Philipp Dufter, Hinrich Schütze
The size of the vocabulary is a central design choice in large pretrained language models, with respect to both performance and memory requirements.
no code implementations • 9 Aug 2023 • Ercong Nie, Helmut Schmid, Hinrich Schütze
However, training an automatic syntactic analysis system for ancient languages solely relying on annotated parse data is a formidable task due to the inherent challenges in building treebanks for such languages.
1 code implementation • 15 Jul 2023 • Bolei Ma, Ercong Nie, Helmut Schmid, Hinrich Schütze
We conduct comprehensive experiments on diverse cross-lingual language understanding tasks (sentiment classification, paraphrase identification, and natural language inference) and empirically analyze the variation trends of prompt-based finetuning performance in cross-lingual transfer across different few-shot and full-data settings.
Natural Language Inference
Natural Language Understanding
+4
1 code implementation • 16 Jun 2023 • Victor Steinborn, Antonis Maronikolakis, Hinrich Schütze
Non-English bias research, however, is still in its infancy with most work focusing on English.
1 code implementation • 26 May 2023 • Yihong Liu, Alexandra Chronopoulou, Hinrich Schütze, Alexander Fraser
By conducting extensive experiments on different language pairs, including similar and distant, high and low-resource languages, we find that our method alleviates the copying problem, thus improving the translation performance on low-resource languages.
no code implementations • 24 May 2023 • Yongkang Liu, Shi Feng, Daling Wang, Yifei Zhang, Hinrich Schütze
There are risks in using eference-free evaluators based on LLMs to evaluate the quality of dialogue responses.
1 code implementation • 24 May 2023 • Xinpeng Wang, Leonie Weissweiler, Hinrich Schütze, Barbara Plank
To the best of our knowledge, this is the first work comprehensively evaluating distillation objectives in both settings.
1 code implementation • 23 May 2023 • Ali Modarressi, Ayyoob Imani, Mohsen Fayyaz, Hinrich Schütze
Large language models (LLMs) have significantly advanced the field of natural language processing (NLP) through their extensive parameters and comprehensive data utilization.
no code implementations • 23 May 2023 • Peiqin Lin, Chengzhi Hu, Zheyu Zhang, André F. T. Martins, Hinrich Schütze
Recent multilingual pretrained language models (mPLMs) have been shown to encode strong language-specific signals, which are not explicitly provided during pretraining.
Open-Ended Question Answering
Zero-Shot Cross-Lingual Transfer
no code implementations • 22 May 2023 • Abdullatif Köksal, Omer Faruk Yalcin, Ahmet Akbiyik, M. Tahir Kilavuz, Anna Korhonen, Hinrich Schütze
Pretrained language models (PLMs) are key components in NLP, but they contain strong social biases.
no code implementations • 22 May 2023 • Haotian Ye, Yihong Liu, Hinrich Schütze
An interesting line of research in natural language processing (NLP) aims to incorporate linguistic typology to bridge linguistic diversity and assist the research of low-resource languages.
1 code implementation • 22 May 2023 • Yihong Liu, Haotian Ye, Leonie Weissweiler, Hinrich Schütze
This demonstrates the benefits of colexification for multilingual NLP.
1 code implementation • 20 May 2023 • Ayyoob Imani, Peiqin Lin, Amir Hossein Kargaran, Silvia Severini, Masoud Jalili Sabet, Nora Kassner, Chunlan Ma, Helmut Schmid, André F. T. Martins, François Yvon, Hinrich Schütze
The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i. e., making them better for about 100 languages.
no code implementations • 15 May 2023 • Chunlan Ma, Ayyoob ImaniGooghari, Haotian Ye, Ehsaneddin Asgari, Hinrich Schütze
While natural language processing tools have been developed extensively for some of the world's languages, a significant portion of the world's over 7000 languages are still neglected.
2 code implementations • 15 May 2023 • Yihong Liu, Haotian Ye, Leonie Weissweiler, Philipp Wicke, Renhao Pei, Robert Zangenfeind, Hinrich Schütze
The resulting measure for the conceptual similarity of two languages is complementary to standard genealogical, typological, and surface similarity measures.
no code implementations • 28 Apr 2023 • Mingyang Wang, Heike Adel, Lukas Lange, Jannik Strötgen, Hinrich Schütze
In this work, we propose to leverage language-adaptive and task-adaptive pretraining on African texts and study transfer learning with source language selection on top of an African language-centric pretrained language model.
5 code implementations • 20 Apr 2023 • Verena Blaschke, Hinrich Schütze, Barbara Plank
This can for instance be observed when finetuning PLMs on one language and evaluating them on data in a closely related language variety with no standardized orthography.
2 code implementations • 19 Apr 2023 • Verena Blaschke, Hinrich Schütze, Barbara Plank
In this work, we instead focus on low-resource languages and in particular non-standardized low-resource languages.
1 code implementation • 17 Apr 2023 • Abdullatif Köksal, Timo Schick, Anna Korhonen, Hinrich Schütze
Our models outperform 10x larger language models without instruction tuning on various tasks such as story/recipe generation and long-form question answering.
no code implementations • 4 Apr 2023 • Antonis Maronikolakis, Abdullatif Köksal, Hinrich Schütze
We introduce HATELEXICON, a lexicon of slurs and targets of hate speech for the countries of Brazil, Germany, India and Kenya, to aid training and interpretability of models.
1 code implementation • 8 Mar 2023 • Amir Hossein Kargaran, Nafiseh Nikeghbal, Abbas Heydarnoori, Hinrich Schütze
Menu system design is a challenging task involving many design options and various human factors.
no code implementations • 4 Feb 2023 • Leonie Weissweiler, Taiqi He, Naoki Otani, David R. Mortensen, Lori Levin, Hinrich Schütze
Construction Grammar (CxG) has recently been used as the basis for probing studies that have investigated the performance of large pretrained language models (PLMs) with respect to the structure and meaning of constructions.
1 code implementation • 19 Dec 2022 • Ercong Nie, Sheng Liang, Helmut Schmid, Hinrich Schütze
Multilingual Pretrained Language Models (MPLMs) have shown their strong multilinguality in recent empirical cross-lingual transfer studies.
no code implementations • 18 Dec 2022 • Yongkang Liu, Shi Feng, Daling Wang, Yifei Zhang, Hinrich Schütze
We investigate response generation for multi-turn dialogue in generative-based chatbots.
1 code implementation • 14 Dec 2022 • Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze
We propose a fully unsupervised method to detect bias in contextualized embeddings.
2 code implementations • Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing 2022 • Sajad Mirzababaei, Amir Hossein Kargaran, Hinrich Schütze, Ehsaneddin Asgari
We create Hengam in the following concrete steps: (1) we develop HengamTagger, an extensible rule-based tool that can extract temporal expressions from a set of diverse language-specific patterns for any language of interest.
Ranked #1 on
Temporal Tagging
on HengamCorpus
no code implementations • 15 Nov 2022 • Abdullatif Köksal, Timo Schick, Hinrich Schütze
Few-shot classification has made great strides due to foundation models that, through priming and prompting, are highly effective few-shot learners.
no code implementations • 25 Oct 2022 • Junze Li, Mengjie Zhao, Yubo Xie, Antonis Maronikolakis, Pearl Pu, Hinrich Schütze
Humor is a magnetic component in everyday human interactions and communications.
no code implementations • 24 Oct 2022 • Leonie Weissweiler, Valentin Hofmann, Abdullatif Köksal, Hinrich Schütze
Construction Grammar (CxG) is a paradigm from cognitive linguistics emphasising the connection between syntax and semantics.
1 code implementation • 18 Oct 2022 • Ayyoob Imani, Silvia Severini, Masoud Jalili Sabet, François Yvon, Hinrich Schütze
An established method for training a POS tagger in such a scenario is to create a labeled training set by transferring from high-resource languages.
1 code implementation • 12 Oct 2022 • Abdullatif Köksal, Silvia Severini, Hinrich Schütze
Word alignments are essential for a variety of NLP tasks.
1 code implementation • 12 Oct 2022 • Yatin Chaudhary, Pranav Rai, Matthias Schubert, Hinrich Schütze, Pankaj Gupta
The objective of Federated Continual Learning (FCL) is to improve deep learning models over life time at each client by (relevant and efficient) knowledge transfer without sharing data.
1 code implementation • 26 Sep 2022 • Peiqin Lin, Jiashuo Wang, Hinrich Schütze, Wenjie Li
To solve the task, it is essential to model the content-emotion duality of a dialogue, which is composed of the content view (i. e., what personal experiences are described) and the emotion view (i. e., the feelings of the speaker on these experiences).
no code implementations • 28 Jul 2022 • Yanai Elazar, Nora Kassner, Shauli Ravfogel, Amir Feder, Abhilasha Ravichander, Marius Mosbach, Yonatan Belinkov, Hinrich Schütze, Yoav Goldberg
Our causal framework and our results demonstrate the importance of studying datasets and the benefits of causality for understanding NLP models.
1 code implementation • 9 Jun 2022 • Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew Dai, Andrew La, Andrew Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakaş, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartłomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, César Ferri Ramírez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Chris Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodola, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, Ethan Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martínez-Plumed, Francesca Happé, Francois Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Wang, Gonzalo Jaimovitch-López, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Shevlin, Hinrich Schütze, Hiromu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Kocoń, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jörg Frohberg, Jos Rozen, Jose Hernandez-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, Katja Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, Kory Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras-Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Colón, Luke Metz, Lütfi Kerem Şenel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ramírez Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, Mátyás Schubert, Medina Orduna Baitemirova, Melody Arnaud, Melvin McElrath, Michael A. Yee, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, Michael Strube, Michał Swędrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T, Nanyun Peng, Nathan A. Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nicole Martinez, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha S. Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Miłkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan LeBras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Ruslan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima, Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven T. Piantadosi, Stuart M. Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsu Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, ZiRui Wang, Ziyi Wu
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
no code implementations • 31 May 2022 • Silvia Severini, Viktor Hangya, Masoud Jalili Sabet, Alexander Fraser, Hinrich Schütze
The two approaches we find most effective are: 1) using identical words as seed lexicons (which unsupervised approaches incorrectly assume are not available for orthographically distinct language pairs) and 2) combining such lexicons with pairs extracted by matching romanized versions of words with an edit distance threshold.
no code implementations • NAACL (GeBNLP) 2022 • Antonis Maronikolakis, Philip Baader, Hinrich Schütze
To tackle the rising phenomenon of hate speech, efforts have been made towards data curation and analysis.
no code implementations • ACL 2022 • Yihong Liu, Haris Jabbar, Hinrich Schütze
The primary novelties of our model are: (a) capturing language-specific sentence representations separately for each language using normalizing flows and (b) using a simple transformation of these latent representations for translating from one language to another.
no code implementations • 31 Mar 2022 • Marina Sedinkina, Martin Schmitt, Hinrich Schütze
The practical success of much of NLP depends on the availability of training data.
1 code implementation • ACL 2022 • Leonie Weissweiler, Valentin Hofmann, Masoud Jalili Sabet, Hinrich Schütze
We introduce CaMEL (Case Marker Extraction without Labels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages.
no code implementations • 17 Mar 2022 • Zhen Han, Ruotong Liao, Jindong Gu, Yao Zhang, Zifeng Ding, Yujia Gu, Heinz Köppl, Hinrich Schütze, Volker Tresp
Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embedding using texts.
no code implementations • Findings (ACL) 2022 • Ayyoob Imani, Lütfi Kerem Şenel, Masoud Jalili Sabet, François Yvon, Hinrich Schütze
First, we create a multiparallel word alignment graph, joining all bilingual word alignment pairs in one graph.
no code implementations • 16 Mar 2022 • Valentin Hofmann, Goran Glavaš, Nikola Ljubešić, Janet B. Pierrehumbert, Hinrich Schütze
Evaluation on three tasks, namely fine-tuned as well as zero-shot geolocation prediction and zero-shot prediction of dialect features, shows that geoadaptation is very effective: e. g., we obtain state-of-the-art performance in supervised geolocation prediction and report massive gains over geographically uninformed PLMs on zero-shot geolocation prediction.
no code implementations • Findings (ACL) 2022 • Sheng Liang, Mengjie Zhao, Hinrich Schütze
Recent research has made impressive progress in large-scale multimodal pre-training.
1 code implementation • ACL 2022 • Lütfi Kerem Senel, Timo Schick, Hinrich Schütze
Pretrained language models (PLMs) have achieved superhuman performance on many benchmarks, creating a need for harder tasks.
no code implementations • 12 Feb 2022 • Yanchen Liu, Timo Schick, Hinrich Schütze
Due to the high costs associated with finetuning large language models, various recent works propose to adapt them to specific tasks without any parameter updates through in-context learning.
no code implementations • LREC 2022 • Silvia Severini, Ayyoob Imani, Philipp Dufter, Hinrich Schütze
Prior work on extracting MNE datasets from parallel corpora required resources such as large monolingual corpora or word aligners that are unavailable or perform poorly for underresourced languages.
no code implementations • Findings (NAACL) 2022 • Mengjie Zhao, Fei Mi, Yasheng Wang, Minglei Li, Xin Jiang, Qun Liu, Hinrich Schütze
We propose LMTurk, a novel approach that treats few-shot learners as crowdsourcing workers.
no code implementations • 26 Nov 2021 • Timo Schick, Hinrich Schütze
Prompt-based approaches are strong at few-shot learning.
no code implementations • EMNLP 2021 • Nora Kassner, Oyvind Tafjord, Hinrich Schütze, Peter Clark
We show that, in a controlled experimental setting, these two mechanisms result in more consistent beliefs in the overall system, improving both the accuracy and consistency of its answers over time.
no code implementations • 28 Sep 2021 • Nikolai Solmsdorf, Dietrich Trautmann, Hinrich Schütze
Despite considerable recent progress, the creation of well-balanced and diverse resources remains a time-consuming and costly challenge in Argument Mining.
no code implementations • 23 Sep 2021 • Maximilian Mozes, Martin Schmitt, Vladimir Golkov, Hinrich Schütze, Daniel Cremers
We investigate the incorporation of visual relationships into the task of supervised image caption generation by proposing a model that leverages detected objects and auto-generated visual relationships to describe images in natural language.
no code implementations • EMNLP (insights) 2021 • Antonis Maronikolakis, Philipp Dufter, Hinrich Schütze
We show that the closer two languages are, the better BERT can align them on the character level.
1 code implementation • 16 Sep 2021 • Sheng Liang, Philipp Dufter, Hinrich Schütze
Multilingual pretrained language models (MPLMs) exhibit multilinguality and are well suited for transfer across languages.
1 code implementation • EMNLP 2021 • Ayyoob Imani, Masoud Jalili Sabet, Lütfi Kerem Şenel, Philipp Dufter, François Yvon, Hinrich Schütze
With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently.
no code implementations • 13 Sep 2021 • Antonis Maronikolakis, Philipp Dufter, Hinrich Schütze
The size of the vocabulary is a central design choice in large pretrained language models, with respect to both performance and memory requirements.
1 code implementation • EMNLP 2021 • Martin Schmitt, Hinrich Schütze
If we allow for tokens outside the PLM's vocabulary, patterns can be adapted more flexibly to a PLM's idiosyncrasies.
Ranked #1 on
Few-Shot NLI
on SherLIiC
1 code implementation • EMNLP 2021 • Mengjie Zhao, Hinrich Schütze
It has been shown for English that discrete and soft prompting perform strongly in few-shot learning with pretrained language models (PLMs).
no code implementations • ACL 2021 • Ayyoob Imani, Masoud Jalili Sabet, Philipp Dufter, Michael Cysouw, Hinrich Schütze
With more than 7000 languages worldwide, multilingual natural language processing (NLP) is essential both from an academic and commercial perspective.
2 code implementations • 2 Jul 2021 • Luisa März, Stefan Schweter, Nina Poerner, Benjamin Roth, Hinrich Schütze
We propose new methods for in-domain and cross-domain Named Entity Recognition (NER) on historical data for Dutch and French.
1 code implementation • Findings (NAACL) 2022 • Valentin Hofmann, Xiaowen Dong, Janet B. Pierrehumbert, Hinrich Schütze
The increasing polarization of online political discourse calls for computational tools that automatically detect and monitor ideological divides in social media.
1 code implementation • NAACL 2021 • Pankaj Gupta, Yatin Chaudhary, Hinrich Schütze
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity in short-text or small collection of documents.
2 code implementations • EMNLP 2021 • Timo Schick, Hinrich Schütze
To obtain high-quality sentence embeddings from pretrained language models (PLMs), they must either be augmented with additional pretraining objectives or finetuned on a large set of labeled text pairs.
Ranked #7 on
Semantic Textual Similarity
on SICK
1 code implementation • NAACL 2021 • Philipp Dufter, Nora Kassner, Hinrich Schütze
Recent research investigates factual knowledge stored in large pretrained language models (PLMs).
1 code implementation • 28 Feb 2021 • Timo Schick, Sahana Udupa, Hinrich Schütze
In this paper, we first demonstrate a surprising finding: pretrained language models recognize, to a considerable degree, their undesirable biases and the toxicity of the content they produce.
no code implementations • CL (ACL) 2022 • Philipp Dufter, Martin Schmitt, Hinrich Schütze
Transformers are arguably the main workhorse in recent Natural Language Processing research.
1 code implementation • EACL 2021 • Martin Schmitt, Hinrich Schütze
Lexical inference in context (LIiC) is the task of recognizing textual entailment between two very similar sentences, i. e., sentences that only differ in one expression.
Ranked #2 on
Few-Shot NLI
on SherLIiC
no code implementations • 9 Feb 2021 • Sahand Sharifzadeh, Sina Moayed Baharlou, Martin Schmitt, Hinrich Schütze, Volker Tresp
We show that by fine-tuning the classification pipeline with the extracted knowledge from texts, we can achieve ~8x more accurate results in scene graph classification, ~3x in object classification, and ~1. 5x in predicate classification, compared to the supervised baselines with only 1% of the annotated images.
no code implementations • 6 Feb 2021 • Lutfi Kerem Senel, Hinrich Schütze
Recent progress in pretraining language models on large corpora has resulted in large performance gains on many NLP tasks.
1 code implementation • 1 Feb 2021 • Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, Yoav Goldberg
In this paper we study the question: Are Pretrained Language Models (PLMs) consistent with respect to factual knowledge?
1 code implementation • EACL 2021 • Nora Kassner, Philipp Dufter, Hinrich Schütze
(i) Can mBERT be used as a multilingual knowledge base?
1 code implementation • ACL 2021 • Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze
How does the input segmentation of pretrained language models (PLMs) affect their interpretations of complex words?
no code implementations • ACL 2021 • Mengjie Zhao, Yi Zhu, Ehsan Shareghi, Ivan Vulić, Roi Reichart, Anna Korhonen, Hinrich Schütze
Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT.
2 code implementations • 22 Dec 2020 • Timo Schick, Hinrich Schütze
Providing pretrained language models with simple task descriptions in natural language enables them to solve some tasks in a fully unsupervised fashion.
no code implementations • 21 Dec 2020 • Ehsaneddin Asgari, Masoud Jalili Sabet, Philipp Dufter, Christopher Ringlstetter, Hinrich Schütze
This method's hypothesis is that the aggregation of different granularities of text for certain language pairs can help word-level alignment.
2 code implementations • COLING 2020 • Timo Schick, Helmut Schmid, Hinrich Schütze
A recent approach for few-shot text classification is to convert textual inputs to cloze questions that contain some form of task description, process them with a pretrained language model and map the predicted words to labels.
1 code implementation • ACL 2021 • Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze
Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena, Vivek Kulkarni, Thomas Runkler, Hinrich Schütze
Our work thus focuses on optimizing the computational cost of fine-tuning for document classification.
5 code implementations • NAACL 2021 • Timo Schick, Hinrich Schütze
When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown et al., 2020) achieve remarkable few-shot performance.
3 code implementations • EMNLP (NLP4ConvAI) 2021 • Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze, Iryna Gurevych
We show that the PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further.
Ranked #1 on
KG-to-Text Generation
on WebNLG (All)
no code implementations • ACL 2019 • Marina Sedinkina, Nikolas Breitkopf, Hinrich Schütze
In our experiments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the financial outcomes excess return and volatility.
1 code implementation • ICML 2020 • Pankaj Gupta, Yatin Chaudhary, Thomas Runkler, Hinrich Schütze
To address the problem, we propose a lifelong learning framework for neural topic modeling that can continuously process streams of document collections, accumulate topics and guide future topic modeling tasks by knowledge transfer from several sources to better deal with the sparse data.
1 code implementation • ICML 2020 • Yatin Chaudhary, Hinrich Schütze, Pankaj Gupta
Marrying topic models and language models exposes language understanding to a broader source of document-level context beyond sentences via topics.
1 code implementation • CONLL 2020 • Nora Kassner, Benno Krojer, Hinrich Schütze
How can pretrained language models (PLMs) learn factual knowledge from the training set?
no code implementations • NAACL (TextGraphs) 2021 • Martin Schmitt, Leonardo F. R. Ribeiro, Philipp Dufter, Iryna Gurevych, Hinrich Schütze
We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation.
Ranked #5 on
KG-to-Text Generation
on AGENDA
no code implementations • 16 May 2020 • Ehsaneddin Asgari, Christoph Ringlstetter, Hinrich Schütze
This paper describes EmbLexChange, a system introduced by the "Life-Language" team for SemEval-2020 Task 1, on unsupervised detection of lexical-semantic changes.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Nora Kassner, Hinrich Schütze
Khandelwal et al. (2020) use a k-nearest-neighbor (kNN) component to improve language model performance.
1 code implementation • EMNLP 2020 • Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze
Can pretrained language models (PLMs) generate derivationally complex words?
1 code implementation • 1 May 2020 • Philipp Dufter, Hinrich Schütze
We aim to identify architectural properties of BERT and linguistic properties of languages that are necessary for BERT to become multilingual.
no code implementations • EMNLP 2020 • Mengjie Zhao, Tao Lin, Fei Mi, Martin Jaggi, Hinrich Schütze
We present an efficient method of utilizing pretrained language models, where we learn selective binary masks for pretrained weights in lieu of modifying them through finetuning.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Mengjie Zhao, Philipp Dufter, Yadollah Yaghoobzadeh, Hinrich Schütze
Pretrained language models have achieved a new state of the art on many NLP tasks, but there are still many open questions about how and why they work so well.
3 code implementations • Findings of the Association for Computational Linguistics 2020 • Masoud Jalili Sabet, Philipp Dufter, François Yvon, Hinrich Schütze
We find that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners, even with abundant parallel data; e. g., contextualized embeddings achieve a word alignment F1 for English-German that is 5 percentage points higher than eflomal, a high-quality statistical aligner, trained on 100k parallel sentences.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Nina Poerner, Ulli Waltinger, Hinrich Schütze
Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by unsupervised pretraining on target-domain text.
6 code implementations • 21 Jan 2020 • Timo Schick, Hinrich Schütze
Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with "task descriptions" in natural language (e. g., Radford et al., 2019).
no code implementations • 7 Jan 2020 • Alena Moiseeva, Dietrich Trautmann, Michael Heimann, Hinrich Schütze
Such intelligent agents can assist the user by answering specific questions and executing routine tasks that are ordinarily performed in a natural language (i. e., customer support).
no code implementations • 12 Dec 2019 • James L. McClelland, Felix Hill, Maja Rudolph, Jason Baldridge, Hinrich Schütze
We take language to be a part of a system for understanding and communicating about situations.
no code implementations • EMNLP 2016 • Ryan Cotterell, Arun Kumar, Hinrich Schütze
Morphological segmentation has traditionally been modeled with non-hierarchical models, which yield flat segmentations as output.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Nina Poerner, Ulli Waltinger, Hinrich Schütze
We present a novel way of injecting factual knowledge about entities into the pretrained BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al., 2016) with BERT's native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors.
no code implementations • ACL 2020 • Nina Poerner, Ulli Waltinger, Hinrich Schütze
We address the task of unsupervised Semantic Textual Similarity (STS) by ensembling diverse pre-trained sentence encoders into sentence meta-embeddings.
2 code implementations • ACL 2020 • Nora Kassner, Hinrich Schütze
We find that PLMs do not distinguish between negated ("Birds cannot [MASK]") and non-negated ("Birds can [MASK]") cloze questions.
1 code implementation • ACL 2020 • Timo Schick, Hinrich Schütze
In this work, we transfer this idea to pretrained language models: We introduce BERTRAM, a powerful architecture based on BERT that is capable of inferring high-quality embeddings for rare words that are suitable as input representations for deep language models.
1 code implementation • WS 2019 • Usama Yaseen, Pankaj Gupta, Hinrich Schütze
Our RE system ranked first in the SeeDev-binary Relation Extraction Task with F1-score of 0. 3738.
1 code implementation • WS 2019 • Yatin Chaudhary, Pankaj Gupta, Hinrich Schütze
This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019.
no code implementations • 1 Oct 2019 • Heike Adel, Hinrich Schütze
In particular, we explore different ways of integrating the named entity types of the relation arguments into a neural network for relation classification, including a joint training and a structured prediction approach.
no code implementations • 25 Sep 2019 • Pankaj Gupta, Yatin Chaudhary, Hinrich Schütze
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collection of documents.
no code implementations • 25 Sep 2019 • Sanjeev Kumar Karn, Francine Chen, Yan-Ying Chen, Ulli Waltinger, Hinrich Schütze
The interleaved posts are encoded hierarchically, i. e., word-to-word (words in a post) followed by post-to-post (posts in a channel).
no code implementations • 14 Sep 2019 • Pankaj Gupta, Yatin Chaudhary, Hinrich Schütze
Though word embeddings and topics are complementary representations, several past works have only used pre-trained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collection of documents.
no code implementations • WS 2019 • Pankaj Gupta, Khushbu Saxena, Usama Yaseen, Thomas Runkler, Hinrich Schütze
To address the tasks of sentence (SLC) and fragment level (FLC) propaganda detection, we explore different neural architectures (e. g., CNN, LSTM-CRF and BERT) and extract linguistic (e. g., part-of-speech, named entity, readability, sentiment, emotion, etc.
no code implementations • 4 Jul 2019 • Ryan Cotterell, Hinrich Schütze
Linguistic similarity is multi-faceted.
1 code implementation • ACL 2019 • Yadollah Yaghoobzadeh, Katharina Kann, Timothy J. Hazen, Eneko Agirre, Hinrich Schütze
Word embeddings typically represent different meanings of a word in a single conflated vector.
no code implementations • 5 Jun 2019 • Sanjeev Kumar Karn, Francine Chen, Yan-Ying Chen, Ulli Waltinger, Hinrich Schütze
Interleaved texts, where posts belonging to different threads occur in one sequence, are a common occurrence, e. g., online chat conversations.
1 code implementation • ACL 2019 • Martin Schmitt, Hinrich Schütze
We present SherLIiC, a testbed for lexical inference in context (LIiC), consisting of 3985 manually annotated inference rule candidates (InfCands), accompanied by (i) ~960k unlabeled InfCands, and (ii) ~190k typed textual relations between Freebase entities extracted from the large entity-linked corpus ClueWeb09.
Ranked #1 on
Lexical Entailment
on SherLIiC
1 code implementation • 22 Apr 2019 • Dietrich Trautmann, Johannes Daxenberger, Christian Stab, Hinrich Schütze, Iryna Gurevych
In this work, we argue that the task should be performed on a more fine-grained level of sequence labeling.
1 code implementation • EMNLP 2020 • Martin Schmitt, Sahand Sharifzadeh, Volker Tresp, Hinrich Schütze
To this end, we present the first approach to unsupervised text generation from KGs and show simultaneously how it can be used for unsupervised semantic parsing.
Ranked #1 on
Unsupervised KG-to-Text Generation
on VG graph-text
1 code implementation • IJCNLP 2019 • Philipp Dufter, Hinrich Schütze
In this work, we investigate three methods for making word spaces interpretable by rotation: Densifier (Rothe et al., 2016), linear SVMs and DensRay, a new method we propose.
2 code implementations • 14 Apr 2019 • Timo Schick, Hinrich Schütze
Pretraining deep neural network architectures with a language modeling objective has brought large improvements for many natural language processing tasks.
1 code implementation • NAACL 2019 • Timo Schick, Hinrich Schütze
Learning high-quality embeddings for rare words is a hard problem because of sparse context information.
1 code implementation • 9 Nov 2018 • Timo Schick, Hinrich Schütze
The general problem setting is that word embeddings are induced on an unlabeled training corpus and then a model is trained that embeds novel words into this induced embedding space.
no code implementations • 6 Nov 2018 • Heike Adel, Hinrich Schütze
Especially, it focuses on the coreference and classification component.
no code implementations • 1 Nov 2018 • Philipp Dufter, Mengjie Zhao, Hinrich Schütze
A simple and effective context-based multilingual embedding learner is Levy et al. (2017)'s S-ID (sentence ID) method.
no code implementations • 31 Oct 2018 • Nina Poerner, Masoud Jalili Sabet, Benjamin Roth, Hinrich Schütze
Count-based word alignment methods, such as the IBM models or fast-align, struggle on very small parallel corpora.
1 code implementation • EMNLP 2018 • Yadollah Yaghoobzadeh, Hinrich Schütze
For representation, we consider representations based on the context distribution of the entity (i. e., on its embedding), on the entity's name (i. e., on its surface form) and on its description in Wikipedia.
1 code implementation • 11 Oct 2018 • Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, Bernt Andrassy, Thomas Runkler
iDepNN models the shortest and augmented dependency paths via recurrent and recursive neural networks to extract relationships within (intra-) and across (inter-) sentence boundaries.
Ranked #1 on
Relation Extraction
on MUC6
1 code implementation • ICLR 2019 • Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schütze
We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i. e., P(word|context): (1) No Language Structure in Context: Probabilistic topic models ignore word order by summarizing a given context as a "bag-of-word" and consequently the semantics of words in the context is lost.
no code implementations • EMNLP 2018 • Katharina Kann, Hinrich Schütze
Neural state-of-the-art sequence-to-sequence (seq2seq) models often do not perform well for small training sets.
2 code implementations • WS 2018 • Nina Poerner, Benjamin Roth, Hinrich Schütze
Input optimization methods, such as Google Deep Dream, create interpretable representations of neurons for computer vision DNNs.
1 code implementation • 15 Sep 2018 • Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schütze
Here, we extend a neural autoregressive topic model to exploit the full context information around words in a document in a language modeling fashion.
no code implementations • NAACL 2019 • Apostolos Kemos, Heike Adel, Hinrich Schütze
Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words.
1 code implementation • 11 Aug 2018 • Pankaj Gupta, Florian Buettner, Hinrich Schütze
Context information around words helps in determining their actual meaning, for example "networks" used in contexts of artificial neural networks or biological neuron networks.
no code implementations • WS 2018 • Pankaj Gupta, Hinrich Schütze
Recurrent neural networks (RNNs) are temporal networks and cumulative in nature that have shown promising results in various natural language processing tasks.
2 code implementations • NAACL 2019 • Sanjeev Kumar Karn, Mark Buckley, Ulli Waltinger, Hinrich Schütze
In this work, we define the task of teaser generation and provide an evaluation benchmark and baseline systems for the process of generating teasers.
1 code implementation • WS 2018 • Yadollah Yaghoobzadeh, Katharina Kann, Hinrich Schütze
We propose a new evaluation method for word embeddings based on multi-label classification given a word embedding.
no code implementations • COLING 2018 • Pankaj Gupta, Bernt Andrassy, Hinrich Schütze
The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi-sentence texts.
no code implementations • COLING 2018 • Wenpeng Yin, Yadollah Yaghoobzadeh, Hinrich Schütze
Large scale knowledge graphs (KGs) such as Freebase are generally incomplete.
1 code implementation • NAACL 2018 • Pankaj Gupta, Benjamin Roth, Hinrich Schütze
Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances.
Relationship Extraction (Distant Supervised)
Vocal Bursts Intensity Prediction
1 code implementation • ACL 2018 • Wenpeng Yin, Hinrich Schütze, Dan Roth
This work deals with SciTail, a natural entailment challenge derived from a multi-choice question answering problem.
no code implementations • NAACL 2018 • Katharina Kann, Manuel Mager, Ivan Meza-Ruiz, Hinrich Schütze
Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce.
no code implementations • 5 Mar 2018 • Benjamin Roth, Costanza Conforti, Nina Poerner, Sanjeev Karn, Hinrich Schütze
In this work, we introduce the task of Open-Type Relation Argument Extraction (ORAE): Given a corpus, a query entity Q and a knowledge base relation (e. g.,"Q authored notable work with title X"), the model has to extract an argument of non-standard entity type (entities that cannot be extracted by a standard named entity tagger, e. g. X: the title of a book or a work of art) from the corpus.
no code implementations • ACL 2018 • Philipp Dufter, Mengjie Zhao, Martin Schmitt, Alexander Fraser, Hinrich Schütze
We present a new method for estimating vector space representations of words: embedding learning by concept induction.
1 code implementation • 19 Jan 2018 • Nina Poerner, Benjamin Roth, Hinrich Schütze
The behavior of deep neural networks (DNNs) is hard to understand.
no code implementations • NAACL 2018 • Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, Bernt Andrassy
We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.
no code implementations • 26 Oct 2017 • Heike Adel, Hinrich Schütze
In this paper, we demonstrate the importance of coreference resolution for natural language processing on the example of the TAC Slot Filling shared task.
1 code implementation • TACL 2018 • Wenpeng Yin, Hinrich Schütze
We hypothesize that this is because the attention in CNNs has been mainly implemented as attentive pooling (i. e., it is applied to pooling) rather than as attentive convolution (i. e., it is integrated into convolution).
no code implementations • 7 Aug 2017 • Yadollah Yaghoobzadeh, Heike Adel, Hinrich Schütze
This paper addresses the problem of corpus-level entity typing, i. e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist".
no code implementations • EMNLP 2017 • Heike Adel, Hinrich Schütze
We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction.
no code implementations • WS 2017 • Katharina Kann, Hinrich Schütze
We present a semi-supervised way of training a character-based encoder-decoder recurrent neural network for morphological reinflection, the task of generating one inflected word form from another.
no code implementations • EMNLP 2017 • Ehsaneddin Asgari, Hinrich Schütze
We present SuperPivot, an analysis method for low-resource languages that occur in a superparallel corpus, i. e., in a corpus that contains an order of magnitude more languages than parallel corpora currently in use.
no code implementations • ACL 2017 • Katharina Kann, Ryan Cotterell, Hinrich Schütze
We present a novel cross-lingual transfer method for paradigm completion, the task of mapping a lemma to its inflected forms, using a neural encoder-decoder model, the state of the art for the monolingual task.
4 code implementations • 7 Feb 2017 • Wenpeng Yin, Katharina Kann, Mo Yu, Hinrich Schütze
Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP).
no code implementations • EACL 2017 • Wenpeng Yin, Hinrich Schütze
This work studies comparatively two typical sentence matching tasks: textual entailment (TE) and answer selection (AS), observing that weaker phrase alignments are more critical in TE, while stronger phrase alignments deserve more attention in AS.
no code implementations • EACL 2017 • Yadollah Yaghoobzadeh, Hinrich Schütze
Entities are essential elements of natural language.
no code implementations • TACL 2018 • Ryan Cotterell, Hinrich Schütze
Since morphology obeys the principle of compositionality, the semantics of the word can be systematically derived from the meaning of its parts.
no code implementations • EACL 2017 • Yadollah Yaghoobzadeh, Heike Adel, Hinrich Schütze
For the second noise type, we propose ways to improve the integration of noisy entity type predictions into relation extraction.
no code implementations • EACL 2017 • Heike Adel, Hinrich Schütze
Neural networks with attention have proven effective for many natural language processing tasks.
no code implementations • EACL 2017 • Katharina Kann, Ryan Cotterell, Hinrich Schütze
We explore the task of multi-source morphological reinflection, which generalizes the standard, single-source version.
no code implementations • EMNLP 2015 • Yadollah Yaghoobzadeh, Hinrich Schütze
This paper addresses the problem of corpus-level entity typing, i. e., inferring from a large corpus that an entity is a member of a class such as "fo