@inproceedings{Zuccon:2015:IEN:2838931.2838936,
 author = {Zuccon, Guido and Koopman, Bevan and Bruza, Peter and Azzopardi, Leif},
 title = "{Integrating and Evaluating Neural Word Embeddings in Information Retrieval}",
 booktitle = {Proceedings of the 20th Australasian Document Computing Symposium},
 series = {ADCS '15},
 year = {2015},
 isbn = {978-1-4503-4040-3},
 location = {Parramatta, NSW, Australia},
 pages = {12:1--12:8},
 articleno = {12},
 numpages = {8},
 url = {http://doi.acm.org/10.1145/2838931.2838936},
 doi = {10.1145/2838931.2838936},
 acmid = {2838936},
 publisher = {ACM},
 address = {New York, NY, USA},
} 



@INPROCEEDINGS{Sandip:2017,
  title="{Regression Based Approaches for Detecting and Measuring Textual Similarity}",
  author={Sarkar, Sandip and Pakray, Partha and Das, Dipankar and Gelbukh, Alexander},
  booktitle={Mining Intelligence and Knowledge Exploration: 4th International Conference, MIKE 2016},
  address={Mexico City},
  pages={pp. 144-152},
  year={2016},
  month={November 13 - 19, 2016},
  organization={Springer International Publishing}
}



@INPROCEEDINGS{sarkar:SemEval,
  author    = {Sarkar, Sandip  and  Das, Dipankar  and  Pakray, Partha  and  Gelbukh, Alexander},
  title     = "{{JUNITMZ} at SemEval-2016 Task 1: Identifying Semantic Similarity Using Levenshtein Ratio}",
  booktitle = {Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)},
  month     = {June},
  year      = {2016},
  address   = {San Diego, California},
  publisher = {Association for Computational Linguistics},
  pages     = {pp. 702--705},
  url       = {http://www.aclweb.org/anthology/S16-1108}
}


@INPROCEEDINGS{sarkar:NITMZ,
  author    = {Sarkar, Sandip  and Saha, Saurav and Bentham, Jereemi and Pakray, Partha and Das, Dipankar and  Gelbukh, Alexander},
  title     = "{{NLP-NITMZ@DPIL-FIRE2016:} Language Independent Paraphrases Detection}",
  booktitle = { Shared task on detecting paraphrases in Indian languages (DPIL), Forum for Information Retrieval Evaluation (FIRE)},
  month     = {December},
  year      = {2016},
  address   = {Kolkata, India},
  pages     = {pp. 256-259}
}



@inproceedings{Gensim,
  added-at = {2013-08-18T12:50:57.000+0200},
  address = {Valletta, Malta},
  author = {{\v R}eh{\r u}{\v r}ek, Radim and Sojka, Petr},
  biburl = {http://www.bibsonomy.org/bibtex/27c4ac4b3886c4d66ee336f4df6bee742/l.sz.},
  booktitle = {{Proceedings of the LREC 2010 Workshop on New           Challenges for NLP Frameworks}},
  day = 22,
  interhash = {c4e9077de9575107abd7b32ee7cad3a2},
  intrahash = {7c4ac4b3886c4d66ee336f4df6bee742},
  keywords = {Gensim LSA Python},
  language = {English},
  month = may,
  pages = {45--50},
  publisher = {ELRA},
  timestamp = {2013-08-18T12:50:57.000+0200},
  title = "{{Software Framework for Topic Modelling with Large Corpora}}",
  year = 2010
}

@article{Elman90,
  added-at = {2012-04-14T00:00:00.000+0200},
  author = {Elman, Jeffrey L.},
  biburl = {http://www.bibsonomy.org/bibtex/2dc7b319fb359091e7e438342ed0c1087/dblp},
  ee = {http://dx.doi.org/10.1207/s15516709cog1402_1},
  interhash = {8770242c4eff4016d2408ab338039b7c},
  intrahash = {dc7b319fb359091e7e438342ed0c1087},
  journal = {Cognitive Science},
  keywords = {dblp},
  number = 2,
  pages = {179-211},
  timestamp = {2012-04-17T12:00:57.000+0200},
  title = "{Finding Structure in Time.}",
  url = {http://dblp.uni-trier.de/db/journals/cogsci/cogsci14.html#Elman90},
  volume = 14,
  year = 1990
}

@InProceedings{agirreSemEval2,
  author    = {Agirre, Eneko  and  Banea, Carmen  and  Cardie, Claire  and  Cer, Daniel  and  Diab, Mona  and  Gonzalez-Agirre, Aitor  and  Guo, Weiwei  and  Lopez-Gazpio, Inigo  and  Maritxalar, Montse  and  Mihalcea, Rada  and  Rigau, German  and  Uria, Larraitz  and  Wiebe, Janyce},
  title     = "{SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability}",
  booktitle = {Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)},
  month     = {June},
  year      = {2015},
  address   = {Denver, Colorado},
  publisher = {Association for Computational Linguistics},
  pages     = {252--263},
  url       = {http://www.aclweb.org/anthology/S15-2045}
}


@article{DBLP:journals/corr/OnalAKR16,
  author    = {Kezban Dilek Onal and
               Ismail Sengor Altingovde and
               Pinar Karagoz and
               Maarten de Rijke},
  title     = "{Getting Started with Neural Models for Semantic Matching in Web Search}",
  journal   = {CoRR},
  volume    = {abs/1611.03305},
  year      = {2016},
  url       = {http://arxiv.org/abs/1611.03305},
  timestamp = {Wed, 07 Jun 2017 14:42:25 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/OnalAKR16},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}


@INPROCEEDINGS{global,
    author = {Jeffrey Pennington and Richard Socher and Christopher D. Manning},
    title = "{Glove: Global vectors for word representation}",
    booktitle = {In EMNLP},
    year = {2014}
}


@article{firth57,
  abstract = {Reprinted in:  Palmer, F. R. (ed.) (1968). Selected Papers of J. R. Firth 1952-59, pages 168-205. Longmans, London. },
  added-at = {2007-10-31T21:25:02.000+0100},
  address = {Oxford},
  author = {Firth, J. R.},
  biburl = {https://www.bibsonomy.org/bibtex/25e3d6c72cdd123a638f71886d78f3c1e/stumme},
  booktitle = {Studies in Linguistic Analysis (special volume of the Philological Society)},
  interhash = {b4f769667fdd195b4a75f61f6388a52e},
  intrahash = {5e3d6c72cdd123a638f71886d78f3c1e},
  keywords = {distributional measure measures relatedness similarity theory},
  pages = {1-32},
  publisher = {The Philological Society},
  timestamp = {2007-10-31T21:25:02.000+0100},
  title = "{A synopsis of linguistic theory 1930-55.}",
  volume = {1952-59},
  year = 1957
}



@inproceedings{SANDIP_IJCNLP,
    title = "{JU} {NITM} at {IJCNLP}-2017 Task 5: A Classification Approach for Answer Selection in Multi-choice Question Answering System",
    author = "Sarkar, Sandip  and
      Das, Dipankar  and
      Pakray, Partha",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://aclanthology.org/I17-4036",
    pages = "213--216",
    abstract = "This paper describes the participation of the JU NITM team in IJCNLP-2017 Task 5: {``}Multi-choice Question Answering in Examinations{''}. The main aim of this shared task is to choose the correct option for each multi-choice question. Our proposed model includes vector representations as feature and machine learning for classification. At first we represent question and answer in vector space and after that find the cosine similarity between those two vectors. Finally we apply classification approach to find the correct answer. Our system was only developed for the English language, and it obtained an accuracy of 40.07{\%} for test dataset and 40.06{\%} for valid dataset.",
}


@article{SANDIP_MCQA,
  author    = {Sandip Sarkar and
               Dipankar Das and
               Partha Pakray and
               David Eduardo Pinto Avenda{\~{n}}o},
  title     = {Developing {MCQA} Framework for Basic Science Subjects using Distributed
               Similarity Model and Classification Based Approaches},
  journal   = {Int. J. Asian Lang. Process.},
  volume    = {30},
  number    = {3},
  pages     = {2050015:1--2050015:18},
  year      = {2020},
  url       = {https://doi.org/10.1142/S2717554520500150},
  doi       = {10.1142/S2717554520500150},
  timestamp = {Thu, 10 Jun 2021 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/journals/jclc/SarkarDPA20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{SANDIP_MATHIR,
  author    = {Amarnath Pathak and
               Partha Pakray and
               Sandip Sarkar and
               Dipankar Das and
               Alexander F. Gelbukh},
  title     = {MathIRs: Retrieval System for Scientific Documents},
  journal   = {Computaci{\'{o}}n y Sistemas},
  volume    = {21},
  number    = {2},
  year      = {2017},
  url       = {http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2743},
  timestamp = {Thu, 11 Feb 2021 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/journals/cys/PathakPSDG17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}




@InProceedings{Sandip_text_sim,
author="Sarkar, Sandip
and Das, Dipankar
and Pakray, Partha
and Pinto, David",
editor="Priyadarshi, Neeraj
and Padmanaban, Sanjeevikumar
and Ghadai, Ranjan Kumar
and Panda, Amiya Ranjan
and Patel, Ranjeeta",
title="A Hybrid Sequential Model for Text Simplification",
booktitle="Advances in Power Systems and Energy Management",
year="2021",
publisher="Springer Singapore",
address="Singapore",
pages="33--42",
abstract="Learning different subjects to enhance knowledge of students and children, reading habit plays an important role. Students often face problems or reading difficulties are aroused when the students are non-native English learners or suffering from dyslexia. Thus, in the present work, we have built a hybrid sequential model for text simplification (i.e. to translate complex English sentences into simple English sentences). Generally, text simplification is treated as monolingual translation task, in which translation occurs between the same languages. In recent trends, encoder-decoder model plays an important role in text simplification. However, a difference exists between normal machine translation and text simplification. Text simplification can be achieved using other different operations like splitting, merging and removing complex word, etc. It is very difficult to deal with the type of operation (i.e. split, delete or replace) that is required for text simplification if we use encoder decoder model. Our encoder-decoder model is mainly based on two different approaches, one is character-based and another is word-based. We also employed a Named Entity Recognizer (NER) to improve the accuracy of our model because named entities do not change while converting a complex sentence into a simple sentence. For learning process, we also use bi-directional encoder-decoder model to improve the result. In the present attempt, we used a well-known text simplification dataset (i.e. PWKP). Our proposed model achieved 0.82 BLUE score using NER module and word-based bi-directional encoder-decoder model.",
isbn="978-981-15-7504-4"
}





@misc{mishra2022numglue,
      title={NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks}, 
      author={Swaroop Mishra and Arindam Mitra and Neeraj Varshney and Bhavdeep Sachdeva and Peter Clark and Chitta Baral and Ashwin Kalyan},
      year={2022},
      eprint={2204.05660},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}


@inproceedings{ravichander-etal-2019-equate,
    title = "{EQUATE}: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference",
    author = "Ravichander, Abhilasha  and
      Naik, Aakanksha  and
      Rose, Carolyn  and
      Hovy, Eduard",
    editor = "Bansal, Mohit  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/K19-1033",
    doi = "10.18653/v1/K19-1033",
    pages = "349--361",
    abstract = "Quantitative reasoning is a higher-order reasoning skill that any intelligent natural language understanding system can reasonably be expected to handle. We present EQUATE (Evaluating Quantitative Understanding Aptitude in Textual Entailment), a new framework for quantitative reasoning in textual entailment. We benchmark the performance of 9 published NLI models on EQUATE, and find that on average, state-of-the-art methods do not achieve an absolute improvement over a majority-class baseline, suggesting that they do not implicitly learn to reason with quantities. We establish a new baseline Q-REAS that manipulates quantities symbolically. In comparison to the best performing NLI model, it achieves success on numerical reasoning tests (+24.2 {\%}), but has limited verbal reasoning capabilities (-8.1 {\%}). We hope our evaluation framework will support the development of models of quantitative reasoning in language understanding.",
}

@inproceedings{chen-etal-2019-numeracy,
    title = "Numeracy-600{K}: Learning Numeracy for Detecting Exaggerated Information in Market Comments",
    author = "Chen, Chung-Chi  and
      Huang, Hen-Hsen  and
      Takamura, Hiroya  and
      Chen, Hsin-Hsi",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'\i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P19-1635",
    doi = "10.18653/v1/P19-1635",
    pages = "6307--6313",
    abstract = "In this paper, we attempt to answer the question of whether neural network models can learn numeracy, which is the ability to predict the magnitude of a numeral at some specific position in a text description. A large benchmark dataset, called Numeracy-600K, is provided for the novel task. We explore several neural network models including CNN, GRU, BiGRU, CRNN, CNN-capsule, GRU-capsule, and BiGRU-capsule in the experiments. The results show that the BiGRU model gets the best micro-averaged F1 score of 80.16{\%}, and the GRU-capsule model gets the best macro-averaged F1 score of 64.71{\%}. Besides discussing the challenges through comprehensive experiments, we also present an important application scenario, i.e., detecting exaggerated information, for the task.",
}




@inproceedings{chen-etal-2023-improving,
    title = "Improving Numeracy by Input Reframing and Quantitative Pre-Finetuning Task",
    author = "Chen, Chung-Chi  and
      Takamura, Hiroya  and
      Kobayashi, Ichiro  and
      Miyao, Yusuke",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-eacl.4",
    doi = "10.18653/v1/2023.findings-eacl.4",
    pages = "69--77",
    abstract = "Numbers have unique characteristics to words. Teaching models to understand numbers in text is an open-ended research question. Instead of discussing the required calculation skills, this paper focuses on a more fundamental topic: understanding numerals. We point out that innumeracy{---}the inability to handle basic numeral concepts{---}exists in most pretrained language models (LMs), and we propose a method to solve this issue by exploring the notation of numbers. Further, we discuss whether changing notation and pre-finetuning along with the comparing-number task can improve performance in three benchmark datasets containing quantitative-related tasks. The results of this study indicate that input reframing and the proposed pre-finetuning task is useful for RoBERTa.",
}



@inproceedings{numeval-report,
    title={SemEval-2024 Task 7: Numeral-Aware Language Understanding and Generation}
    author={Chen, Chung-Chi and Huang, Jian-Tao and Huang, Hen-Hsen and Takamura, Hiroya and Chen, Hsin-Hsi},
    booktitle={Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)},
    year={2024},
    publisher={Association for Computational Linguistics},
}



@inproceedings{chen-etal-2023-improving,
    title = "Improving Numeracy by Input Reframing and Quantitative Pre-Finetuning Task",
    author = "Chen, Chung-Chi  and
      Takamura, Hiroya  and
      Kobayashi, Ichiro  and
      Miyao, Yusuke",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-eacl.4",
    doi = "10.18653/v1/2023.findings-eacl.4",
    pages = "69--77"
}


@inproceedings{mishra-etal-2022-numglue,
    title = "{N}um{GLUE}: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks",
    author = "Mishra, Swaroop  and
      Mitra, Arindam  and
      Varshney, Neeraj  and
      Sachdeva, Bhavdeep  and
      Clark, Peter  and
      Baral, Chitta  and
      Kalyan, Ashwin",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.246",
    doi = "10.18653/v1/2022.acl-long.246",
    pages = "3505--3523"
}