pwc-archive/papers-with-abstracts huggingface dataset:
A New Broad NLP Training from Speech to Knowledge
Authors: Maxime Amblard, Miguel Couceiro
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6
Tasks: Management

In 2018, the Master Sc. in NLP opened at IDMC - Institut des Sciences du Digital, du Management et de la Cognition, Université de Lorraine - Nancy, France. Far from being a creation ex-nihilo, it is the product of a history and many reflections on the field and its teaching. This article proposes epistemological and critical elements on the opening and maintainance of this so far new master’s program in NLP.
A Crash Course on Ethics for Natural Language Processing
Authors: Annemarie Friedrich, Torsten Zesch
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6
Tasks: Ethics

It is generally agreed upon in the natural language processing (NLP) community that ethics should be integrated into any curriculum. Being aware of and understanding the relevant core concepts is a prerequisite for following and participating in the discourse on ethical NLP. We here present ready-made teaching material in the form of slides and practical exercises on ethical issues in NLP, which is primarily intended to be integrated into introductory NLP or computational linguistics courses. By making this material freely available, we aim at lowering the threshold to adding ethics to the curriculum. We hope that increased awareness will enable students to identify potentially unethical behavior.
MiniVQA - A resource to build your tailored VQA competition
Authors: Jean-Benoit Delbrouck
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6
Tasks: BIG-bench Machine Learning, Visual Question Answering (VQA)

MiniVQA is a Jupyter notebook to build a tailored VQA competition for your students. The resource creates all the needed resources to create a classroom competition that engages and inspires your students on the free, self-service Kaggle platform. “InClass competitions make machine learning fun¡‘.
From back to the roots into the gated woods: Deep learning for NLP
Authors: Barbara Plank
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6
Tasks: Decoder

Deep neural networks have revolutionized many fields, including Natural Language Processing. This paper outlines teaching materials for an introductory lecture on deep learning in Natural Language Processing (NLP). The main submitted material covers a summer school lecture on encoder-decoder models. Complementary to this is a set of jupyter notebook slides from earlier teaching, on which parts of the lecture were based on. The main goal of this teaching material is to provide an overview of neural network approaches to natural language processing, while linking modern concepts back to the roots showing traditional essential counterparts. The lecture departs from count-based statistical methods and spans up to gated recurrent networks and attention, which is ubiquitous in today’s NLP.
Learning PyTorch Through A Neural Dependency Parsing Exercise
Authors: David Jurgens
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6
Tasks: Dependency Parsing

Dependency parsing is increasingly the popular parsing formalism in practice. This assignment provides a practice exercise in implementing the shift-reduce dependency parser of Chen and Manning (2014). This parser is a two-layer feed-forward neural network, which students implement in PyTorch, providing practice in developing deep learning models and exposure to developing parser models.
A Balanced and Broadly Targeted Computational Linguistics Curriculum
Authors: Emma Manning, Nathan Schneider, Amir Zeldes
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6

This paper describes the primarily-graduate computational linguistics and NLP curriculum at Georgetown University, a U.S. university that has seen significant growth in these areas in recent years. We reflect on the principles behind our curriculum choices, including recognizing the various academic backgrounds and goals of our students; teaching a variety of skills with an emphasis on working directly with data; encouraging collaboration and interdisciplinary work; and including languages beyond English. We reflect on challenges we have encountered, such as the difficulty of teaching programming skills alongside NLP fundamentals, and discuss areas for future growth.
Gaining Experience with Structured Data: Using the Resources of Dialog State Tracking Challenge 2
Authors: Ronnie Smith
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6
Tasks: dialog state tracking

This paper describes a class project for a recently introduced undergraduate NLP course that gives computer science students the opportunity to explore the data of Dialog State Tracking Challenge 2 (DSTC 2). Student background, curriculum choices, and project details are discussed. The paper concludes with some instructor advice and final reflections.
Flamingos and Hedgehogs in the Croquet-Ground: Teaching Evaluation of NLP Systems for Undergraduate Students
Authors: Brielen Madureira
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6

This report describes the course Evaluation of NLP Systems, taught for Computational Linguistics undergraduate students during the winter semester 20/21 at the University of Potsdam, Germany. It was a discussion-based seminar that covered different aspects of evaluation in NLP, namely paradigms, common procedures, data annotation, metrics and measurements, statistical significance testing, best practices and common approaches in specific NLP tasks and applications.
An Immersive Computational Text Analysis Course for Non-Computer Science Students at Barnard College
Authors: Adam Poliak, Jalisha Jenifer
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6

We provide an overview of a new Computational Text Analysis course that will be taught at Barnard College over a six week period in May and June 2021. The course is targeted to non Computer Science at a Liberal Arts college that wish to incorporate fundamental Natural Language Processing tools in their re- search and studies. During the course, students will complete daily programming tutorials, read and review contemporary research papers, and propose and develop independent research projects.
Contemporary NLP Modeling in Six Comprehensive Programming Assignments
Authors: Greg Durrett, Jifan Chen, Shrey Desai, Tanya Goyal, Lucas Kabela, Yasumasa Onoe, Jiacheng Xu
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6

We present a series of programming assignments, adaptable to a range of experience levels from advanced undergraduate to PhD, to teach students design and implementation of modern NLP systems. These assignments build from the ground up and emphasize full-stack understanding of machine learning models: initially, students implement inference and gradient computation by hand, then use PyTorch to build nearly state-of-the-art neural networks using current best practices. Topics are chosen to cover a wide range of modeling and inference techniques that one might encounter, ranging from linear models suitable for industry applications to state-of-the-art deep learning models used in NLP research. The assignments are customizable, with constrained options to guide less experienced students or open-ended options giving advanced students freedom to explore. All of them can be deployed in a fully autogradable fashion, and have collectively been tested on over 300 students across several semesters.
Interactive Assignments for Teaching Structured Neural NLP
Authors: David Gaddy, Daniel Fried, Nikita Kitaev, Mitchell Stern, Rodolfo Corona, John DeNero, Dan Klein
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6

We present a set of assignments for a graduate-level NLP course. Assignments are designed to be interactive, easily gradable, and to give students hands-on experience with several key types of structure (sequences, tags, parse trees, and logical forms), modern neural architectures (LSTMs and Transformers), inference algorithms (dynamic programs and approximate search) and training methods (full and weak supervision). We designed assignments to build incrementally both within each assignment and across assignments, with the goal of enabling students to undertake graduate-level research in NLP by the end of the course.
Learning about Word Vector Representations and Deep Learning through Implementing Word2vec
Authors: David Jurgens
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6

Word vector representations are an essential part of an NLP curriculum. Here, we describe a homework that has students implement a popular method for learning word vectors, word2vec. Students implement the core parts of the method, including text preprocessing, negative sampling, and gradient descent. Starter code provides guidance and handles basic operations, which allows students to focus on the conceptually challenging aspects. After generating their vectors, students evaluate them using qualitative and quantitative tests.
Naive Bayes versus BERT: Jupyter notebook assignments for an introductory NLP course
Authors: Jennifer Foster, Joachim Wagner
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6

We describe two Jupyter notebooks that form the basis of two assignments in an introductory Natural Language Processing (NLP) module taught to final year undergraduate students at Dublin City University. The notebooks show the students how to train a bag-of-words polarity classifier using multinomial Naive Bayes, and how to fine-tune a polarity classifier using BERT. The students take the code as a starting point for their own experiments.
Natural Language Processing for Computer Scientists and Data Scientists at a Large State University
Authors: Casey Kennington
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6

The field of Natural Language Processing (NLP) changes rapidly, requiring course offerings to adjust with those changes, and NLP is not just for computer scientists; it’s a field that should be accessible to anyone who has a sufficient background. In this paper, I explain how students with Computer Science and Data Science backgrounds can be well-prepared for an upper-division NLP course at a large state university. The course covers probability and information theory, elementary linguistics, machine and deep learning, with an attempt to balance theoretical ideas and concepts with practical applications. I explain the course objectives, topics and assignments, reflect on adjustments to the course over the last four years, as well as feedback from students.
On Writing a Textbook on Natural Language Processing
Authors: Jacob Eisenstein
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6

There are thousands of papers about natural language processing and computational linguistics, but very few textbooks. I describe the motivation and process for writing a college textbook on natural language processing, and offer advice and encouragement for readers who may be interested in writing a textbook of their own.
Learning How To Learn NLP: Developing Introductory Concepts Through Scaffolded Discovery
Authors: Alexandra Schofield, Richard Wicentowski, Julie Medero
Date: 2021-06-01 00:00:00
Proceeding: NAACL (TeachingNLP) 2021 6

We present a scaffolded discovery learning approach to introducing concepts in a Natural Language Processing course aimed at computer science students at liberal arts institutions. We describe some of the objectives of this approach, as well as presenting specific ways that four of our discovery-based assignments combine specific natural language processing concepts with broader analytic skills. We argue this approach helps prepare students for many possible future paths involving both application and innovation of NLP technology by emphasizing experimental data navigation, experiment design, and awareness of the complexities and challenges of analysis.
Meta-Reinforcement Learning for Mastering Multiple Skills and Generalizing across Environments in Text-based Games
Authors: Zhenjie Zhao, Mingfei Sun, Xiaojuan Ma
Date: 2021-08-01 00:00:00
Proceeding: ACL (MetaNLP) 2021 8
Tasks: Imitation Learning, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), text-based games

Text-based games can be used to develop task-oriented text agents for accomplishing tasks with high-level language instructions, which has potential applications in domains such as human-robot interaction. Given a text instruction, reinforcement learning is commonly used to train agents to complete the intended task owing to its convenience of learning policies automatically. However, because of the large space of combinatorial text actions, learning a policy network that generates an action word by word with reinforcement learning is challenging. Recent research works show that imitation learning provides an effective way of training a generation-based policy network. However, trained agents with imitation learning are hard to master a wide spectrum of task types or skills, and it is also difficult for them to generalize to new environments. In this paper, we propose a meta reinforcement learning based method to train text agents through learning-to-explore. In particular, the text agent first explores the environment to gather task-specific information and then adapts the execution policy for solving the task with this information. On the publicly available testbed ALFWorld, we conducted a comparison study with imitation learning and show the superiority of our method.
Multi-Pair Text Style Transfer for Unbalanced Data via Task-Adaptive Meta-Learning
Authors: Xing Han, Jessica Lundin
Date: 2021-08-01 00:00:00
Proceeding: ACL (MetaNLP) 2021 8
Tasks: Meta-Learning, Sentence, Style Transfer, Text Style Transfer

Text-style transfer aims to convert text given in one domain into another by paraphrasing the sentence or substituting the keywords without altering the content. By necessity, state-of-the-art methods have evolved to accommodate nonparallel training data, as it is frequently the case there are multiple data sources of unequal size, with a mixture of labeled and unlabeled sentences. Moreover, the inherent style defined within each source might be distinct. A generic bidirectional (e.g., formal \Leftrightarrow informal) style transfer regardless of different groups may not generalize well to different applications. In this work, we developed a task adaptive meta-learning framework that can simultaneously perform a multi-pair text-style transfer using a single model. The proposed method can adaptively balance the difference of meta-knowledge across multiple tasks. Results show that our method leads to better quantitative performance as well as coherent style variations. Common challenges of unbalanced data and mismatched domains are handled well by this method.
Meta-Learning for Few-Shot Named Entity Recognition
Authors: Cyprien de Lichy, Hadrien Glaude, William Campbell
Date: 2021-08-01 00:00:00
Proceeding: ACL (MetaNLP) 2021 8
Tasks: Language Modeling, Language Modelling, Meta-Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Structured Prediction

Meta-learning has recently been proposed to learn models and algorithms that can generalize from a handful of examples. However, applications to structured prediction and textual tasks pose challenges for meta-learning algorithms. In this paper, we apply two meta-learning algorithms, Prototypical Networks and Reptile, to few-shot Named Entity Recognition (NER), including a method for incorporating language model pre-training and Conditional Random Fields (CRF). We propose a task generation scheme for converting classical NER datasets into the few-shot setting, for both training and evaluation. Using three public datasets, we show these meta-learning algorithms outperform a reasonable fine-tuned BERT baseline. In addition, we propose a novel combination of Prototypical Networks and Reptile.
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification
Authors: Yue Li, Jiong Zhang
Date: 2021-08-01 00:00:00
Proceeding: ACL (MetaNLP) 2021 8
Tasks: Cross-Domain Few-Shot, Few-Shot Learning, intent-classification, Intent Classification, Meta-Learning, Multi-class Classification, Sentence

Meta learning aims to optimize the model’s capability to generalize to new tasks and domains. Lacking a data-efficient way to create meta training tasks has prevented the application of meta-learning to the real-world few shot learning scenarios. Recent studies have proposed unsupervised approaches to create meta-training tasks from unlabeled data for free, e.g., the SMLMT method (Bansal et al., 2020a) constructs unsupervised multi-class classification tasks from the unlabeled text by randomly masking words in the sentence and let the meta learner choose which word to fill in the blank. This study proposes a semi-supervised meta-learning approach that incorporates both the representation power of large pre-trained language models and the generalization capability of prototypical networks enhanced by SMLMT. The semi-supervised meta training approach avoids overfitting prototypical networks on a small number of labeled training examples and quickly learns cross-domain task-specific representation only from a few supporting examples. By incorporating SMLMT with prototypical networks, the meta learner generalizes better to unseen domains and gains higher accuracy on out-of-scope examples without the heavy lifting of pre-training. We observe significant improvement in few-shot generalization after training only a few epochs on the intent classification tasks evaluated in a multi-domain setting.
Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional Categories
Authors: Gaël Guibon, Matthieu Labeau, Hélène Flamein, Luce Lefeuvre, Chloé Clavel
Date: 2021-08-01 00:00:00
Proceeding: ACL (MetaNLP) 2021 8
Tasks: Few-Shot Learning, Meta-Learning, Metric Learning, TAG

In this paper, we place ourselves in a classification scenario in which the target classes and data type are not accessible during training. We use a meta-learning approach to determine whether or not meta-trained information from common social network data with fine-grained emotion labels can achieve competitive performance on messages labeled with different emotion categories. We leverage few-shot learning to match with the classification scenario and consider metric learning based meta-learning by setting up Prototypical Networks with a Transformer encoder, trained in an episodic fashion. This approach proves to be effective for capturing meta-information from a source emotional tag set to predict previously unseen emotional tags. Even though shifting the data type triggers an expected performance drop, our meta-learning approach achieves decent results when compared to the fully supervised one.
The Third Multilingual Surface Realisation Shared Task (SR’20): Overview and Evaluation Results
Authors: Simon Mille, Anya Belz, Bernd Bohnet, Thiago castro Ferreira, Yvette Graham, Leo Wanner
Date: 2020-12-01 00:00:00
Proceeding: MSR (COLING) 2020 12

This paper presents results from the Third Shared Task on Multilingual Surface Realisation (SR’20) which was organised as part of the COLING’20 Workshop on Multilingual Surface Realisation. As in SR’18 and SR’19, the shared task comprised two tracks: (1) a Shallow Track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (2) a Deep Track where additionally, functional words and morphological information were removed. Moreover, each track had two subtracks: (a) restricted-resource, where only the data provided or approved as part of a track could be used for training models, and (b) open-resource, where any data could be used. The Shallow Track was offered in 11 languages, whereas the Deep Track in 3 ones. Systems were evaluated using both automatic metrics and direct assessment by human evaluators in terms of Readability and Meaning Similarity to reference outputs. We present the evaluation results, along with descriptions of the SR’19 tracks, data and evaluation methods, as well as brief summaries of the participating systems. For full descriptions of the participating systems, please see the separate system reports elsewhere in this volume.
BME-TUW at SR’20: Lexical grammar induction for surface realization
Authors: Gábor Recski, Ádám Kovács, Kinga Gémes, Judit Ács, Andras Kornai
Date: 2020-12-01 00:00:00
Proceeding: MSR (COLING) 2020 12
Tasks: Decoder, Sentence

We present a system for mapping Universal Dependency structures to raw text which learns to restore word order by training an Interpreted Regular Tree Grammar (IRTG) that establishes a mapping between string and graph operations. The reinflection step is handled by a standard sequence-to-sequence architecture with a biLSTM encoder and an LSTM decoder with attention. We modify our 2019 system (Kovács et al., 2019) with a new grammar induction mechanism that allows IRTG rules to operate on lemmata in addition to part-of-speech tags and ensures that each word and its dependents are reordered using the most specific set of learned patterns. We also introduce a hierarchical approach to word order restoration that independently determines the word order of each clause in a sentence before arranging them with respect to the main clause, thereby improving overall readability and also making the IRTG parsing task tractable. We participated in the 2020 Surface Realization Shared task, subtrack T1a (shallow, closed). Human evaluation shows we achieve significant improvements on two of the three out-of-domain datasets compared to the 2019 system we modified. Both components of our system are available on GitHub under an MIT license.
ADAPT at SR’20: How Preprocessing and Data Augmentation Help to Improve Surface Realization
Authors: Henry Elder
Date: 2020-12-01 00:00:00
Proceeding: MSR (COLING) 2020 12
Tasks: Data Augmentation

In this paper, we describe the ADAPT submission to the Surface Realization Shared Task 2020. We present a neural-based system trained on the English Web Treebank and an augmented dataset, automatically created from existing text corpora.
IMSurReal Too: IMS in the Surface Realization Shared Task 2020
Authors: Xiang Yu, Simon Tannert, Ngoc Thang Vu, Jonas Kuhn
Date: 2020-12-01 00:00:00
Proceeding: MSR (COLING) 2020 12
Tasks: Data Augmentation

We introduce the IMS contribution to the Surface Realization Shared Task 2020. The new system achieves substantial improvement over the state-of-the-art system from last year, mainly due to a better token representation and a better linearizer, as well as a simple ensembling approach. We also experiment with data augmentation, which brings some additional performance gain. The system is available at https://github.com/EggplantElf/IMSurReal.
Lexical Induction of Morphological and Orthographic Forms for Low-Resourced Languages
Authors: Taha Tobaili
Date: 2020-12-01 00:00:00
Proceeding: MSR (COLING) 2020 12
Tasks: Word Embeddings

In this work we address the issue of high-degree lexical sparsity for non-standard languages under severe circumstance of small resources that are considered insufficient to train recent powerful language models. We proposed a new rule-based approach and utilised word embeddings to connect words with their inflectional and orthographic forms from a given corpus. Our case example is the low-resourced Lebanese dialect Arabizi. Arabizi is the name given to a new social transcription of the spoken Arabic in Latin script. The term comes from the portmanteau of Araby (Arabic) and Englizi (English). It is an informal written language where Arabs transcribe their dialectal mother tongue in text using Latin alphanumeral instead of Arabic script. For example حبيبي Ḥabībī my-love could be transcribed as 7abibi in Arabizi. We induced 175K forms from a list of 1.7K sentiment words. We evaluated this induction extrinsically on a sentiment-annotated dataset pushing its coverage by 13% over the previous version. We named the new lexicon SenZi-Large and released it publicly.