This is a curated list of papers that I have encountered in some capacity and deem worth including in the NLP practitioner's library. Some papers may appear in multiple sub-categories, if they don't fit easily into one of the boxes.

PRs are absolutely welcome!

Some special designations for certain papers:

💡 LEGEND: This is a game-changer in the NLP literature and worth reading.

📼 RESOURCE: This paper introduces some dataset/resource and hence may be useful for application purposes.

• (2000) A Statistical Part-of-Speech Tagger
  • TLDR: Seminal paper demonstrating a powerful HMM-based POS tagger. Many tips and tricks for building such classical systems included.
• (2003) Feature-rich part-of-speech tagging with a cyclic dependency network
  • TLDR: Proposes a number of powerful linguistic features for building a (then) SOTA POS-tagging system
• (2015) Bidirectional LSTM-CRF Models for Sequence Tagging
  • TLDR: Proposes an element sequence-tagging model combining neural networks with conditional random fields, achieving SOTA in POS-tagging, NER, and chunking.

• (2003) Accurate unlexicalized parsing 💡
  • TLDR: Beautiful paper demonstrating that unlexicalized probabilistic context free grammars can exceed the performance of lexicalized PCFGs.
• (2014) A Fast and Accurate Dependency Parser using Neural Networks
  • TLDR: Very important work ushering in a new wave of neural network-based parsing architectures, achieving SOTA performance as well as blazing parsing speeds.

• (2005) Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling
  • TLDR: Using cool Monte Carlo methods combined with a conditional random field model, this work achieves a huge error reduction in certain information extraction benchmarks.
• (2015) Bidirectional LSTM-CRF Models for Sequence Tagging
  • TLDR: Proposes an element sequence-tagging model combining neural networks with conditional random fields, achieving SOTA in POS-tagging, NER, and chunking.

• (2010) A multi-pass sieve for coreference resolution 💡
  • TLDR: Proposes a sieve-based approach to coreference resolution that for many years (until deep learning approaches) was SOTA.
• (2015) Entity-Centric Coreference Resolution with Model Stacking
  • TLDR: This work offers a nifty approach to building coreference chains iteratively using entity-level features.
• (2016) Improving Coreference Resolution by Learning Entity-Level Distributed Representations
  • TLDR: One of the earliest effective approaches to using neural networks for coreference resolution, significantly outperforming the SOTA.

• (2012) Baselines and Bigrams: Simple, Good Sentiment and Topic Classification
  • TLDR: Very elegant paper, illustrating that simple Naive Bayes models with bigram features can outperform more sophisticated methods like support vector machines on tasks such as sentiment analysis.
• (2013) Recursive deep models for semantic compositionality over a sentiment treebank 📼
  • TLDR: Introduces the Stanford Sentiment Treebank, a wonderful resource for fine-grained sentiment annotation on sentences. Also introduces the Recursive Neural Tensor Network, a neat linguistically-motivated deep learning architecture.

• (2007) Natural Logic for Textual Inference
  • TLDR: Proposes a rigorous logic-based approach to the problem of textual inference called natural logic. Very cool mathematically-motivated transforms are used to deduce the relationship between phrases.
• (2008) An Extended Model of Natural Logic
  • TLDR: Extends previous work on natural logic for inference, adding phenomena such as semantic exclusion and implicativity to enhance the premise-hypothesis transform process.
• (2014) Recursive Neural Networks Can Learn Logical Semantics
  • TLDR: Demonstrates that deep learning architectures such as neural tensor networks can effectively be applied to natural language inference.
• (2015) A large annotated corpus for learning natural language inference 📼
  • TLDR: Introduces the Stanford Natural Language Inference corpus, a wonderful NLI resource larger by two orders of magnitude over previous datasets.

• (1993) The Mathematics of Statistical Machine Translation 💡
  • TLDR: Introduces the IBM machine translation models, several seminal models in statistical MT.
• (2002) BLEU: A Method for Automatic Evaluation of Machine Translation 📼
  • TLDR: Proposes BLEU, the defacto evaluation technique used for machine translation (even today!)
• (2003) Statistical Phrase-Based Translation
  • TLDR: Introduces a phrase-based translation model for MT, doing nice analysis that demonstrates why phrase-based models outperform word-based ones.
• (2014) Sequence to Sequence Learning with Neural Networks 💡
  • TLDR: Introduces the sequence-to-sequence neural network architecture. While only applied to MT in this paper, it has since become one of the cornerstone architectures of modern natural language processing.
• (2015) Neural Machine Translation by Jointly Learning to Align and Translate 💡
  • TLDR: Extends previous sequence-to-sequence architectures for MT by using the attention mechanism, a powerful tool for allowing a target word to softly search for important signal from the source sentence.
• (2015) Effective approaches to attention-based neural machine translation
  • TLDR: Introduces two new attention mechanisms for MT, using them to achieve SOTA over existing neural MT systems.
• (2016) Neural Machine Translation of Rare Words with Subword Units
  • TLDR: Introduces byte pair encoding, an effective technique for allowing neural MT systems to handle (more) open-vocabulary translation.
• (2016) Pointing the Unknown Words
  • TLDR: Proposes a copy-mechanism for allowing MT systems to more effectively copy words from a source context sequence.
• (2016) Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
  • TLDR: A wonderful case-study demonstrating what a production-capacity machine translation system (in this case that of Google) looks like.

• (2013) Semantic Parsing on Freebase from Question-Answer Pairs 💡 📼
  • TLDR: Proposes an elegant technique for semantic parsing that learns directly from question-answer pairs, without the need for annotated logical forms, allowing the system to scale up to Freebase.
• (2014) Semantic Parsing via Paraphrasing
  • TLDR: Develops a unique paraphrase model for learning appropriate candidate logical forms from question-answer pairs, improving SOTA on existing Q/A datasets.
• (2015) Building a Semantic Parser Overnight 📼
  • TLDR: Neat paper showing that a semantic parser can be built from scratch starting with no training examples!
• (2015) Bringing Machine Learning and Computational Semantics Together
  • TLDR: A nice overview of a computational semantics framework that uses machine learning to effectively learn logical forms for semantic parsing.

• (2016) A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
  • TLDR: A great wake-up call paper, demonstrating that SOTA performance can be achieved on certain reading comprehension datasets using simple systems with carefully chosen features. Don't forget non-deep learning methods!
• (2017) SQuAD: 100,000+ Questions for Machine Comprehension of Text 📼
  • TLDR: Introduces the SQUAD dataset, a question-answering corpus that has become one of the defacto benchmarks used today.

• (2004) ROUGE: A Package for Automatic Evaluation of Summaries 📼
  • TLDR: Introduces ROUGE, an evaluation metric for summarization that is used to this day on a variety of sequence transduction tasks.
• (2015) Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
  • TLDR: Proposes a neural natural language generator that jointly optimises sentence planning and surface realization, outperforming other systems on human eval.
• (2016) Pointing the Unknown Words
  • TLDR: Proposes a copy-mechanism for allowing MT systems to more effectively copy words from a source context sequence.
• (2017) Get To The Point: Summarization with Pointer-Generator Networks
  • TLDR: This work offers an elegant soft copy mechanism, that drastically outperforms the SOTA on abstractive summarization.

• (2011) Data-drive Response Generation in Social Media
  • TLDR: Proposes using phrase-based statistical machine translation methods to the problem of response generation.
• (2015) Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
  • TLDR: Proposes a neural natural language generator that jointly optimises sentence planning and surface realization, outperforming other systems on human eval.
• (2016) How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation 💡
  • TLDR: Important work demonstrating that existing automatic metrics used for dialogue woefully do not correlate well with human judgment.
• (2016) A Network-based End-to-End Trainable Task-oriented Dialogue System
  • TLDR: Proposes a neat architecture for decomposing a dialogue system into a number of individually-trained neural network components.
• (2016) A Diversity-Promoting Objective Function for Neural Conversation Models
  • TLDR: Introduces a maximum mutual information objective function for training dialogue systems.
• (2016) The Dialogue State Tracking Challenge Series: A Review
  • TLDR: A nice overview of the dialogue state tracking challenges for dialogue systems.
• (2017) A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue
  • TLDR: Shows that simple sequence-to-sequence architectures with a copy mechanism can perform competitively on existing task-oriented dialogue datasets.
• (2017) Key-Value Retrieval Networks for Task-Oriented Dialogue 📼
  • TLDR: Introduces a new multidomain dataset for task-oriented dataset as well as an architecture for softly incorporating information from structured knowledge bases into dialogue systems.
• (2017) Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings 📼
  • TLDR: Introduces a new collaborative dialogue dataset, as well as an architecture for representing structured knowledge via knowledge graph embeddings.
• (2017) Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
  • TLDR: Introduces a hybrid dialogue architecture that can be jointly trained via supervised learning as well as reinforcement learning and combines neural network techniques with fine-grained rule-based approaches.

• (1971) Procedures as a Representation for Data in a Computer Program for Understanding Natural Language
  • TLDR: One of the seminal papers in computer science, introducing SHRDLU an early system for computers understanding human language commands.
• (2016) Learning language games through interaction
  • TLDR: Introduces a novel setting for interacting with computers to accomplish a task where only natural language can be used to communicate with the system!
• (2017) Naturalizing a programming language via interactive learning
  • TLDR: Very cool work allowing a community of workers to iteratively naturalize a language starting with a core set of commands in an interactive task.

• (1996) An Empirical Study of Smoothing Techniques for Language Modelling
  • TLDR: Performs an extensive survey of smoothing techniques in traditional language modelling systems.
• (2003) A Neural Probabilistic Language Model 💡
  • TLDR: A seminal work in deep learning for NLP, introducing one of the earliest effective models for neural network-based language modelling.
• (2014) One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling 📼
  • TLDR: Introduces the Google One Billion Word language modelling benchmark.
• (2015) Character-Aware Neural Language Models
  • TLDR: Proposes a language model using convolutional neural networks that can employ character-level information, performing on-par with word-level LSTM systems.
• (2016) Exploring the Limits of Language Modeling
  • TLDR: Introduces a mega language model system using deep learning that uses a variety of techniques and significantly performs the SOTA on the One Billion Words Benchmark.
• (2018) Deep contextualized word representations 💡 📼
  • TLDR: This paper introduces ELMO, a super powerful collection of word embeddings learned from the intermediate representations of a deep bidirectional LSTM language model. Achieved SOTA on 6 diverse NLP tasks.
• (2018) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding 💡
  • TLDR: One of the most important papers of 2018, introducing BERT a powerful architecture pretrained using language modelling which is then effectively transferred to other domain-specific tasks.
• (2019) XLNet: Generalized Autoregressive Pretraining for Language Understanding 💡
  • TLDR: Generalized autoregressive pretraining method that improves upon BERT by maximizing the expected likelihood over all permutations of the factorization order.

• (1997) Long Short-Term Memory 💡
  • TLDR: Introduces the LSTM recurrent unit, a cornerstone of modern neural network-based NLP
• (2000) Maximum Entropy Markov Models for Information Extraction and Segmentation 💡
  • TLDR: Introduces Markov Entropy Markov models for information extraction, a commonly used ML technique in classical NLP.
• (2010) From Frequency to Meaning: Vector Space Models of Semantics
  • TLDR: A wonderful survey of existing vector space models for learning semantics in text.
• (2012) An Introduction to Conditional Random Fields
  • TLDR: A nice, in-depth overview of conditional random fields, a commonly-used sequence-labelling model.
• (2014) Glove: Global vectors for word representation 💡 📼
  • TLDR: Introduces Glove word embeddings, one of the most commonly used pretrained word embedding techniques across all flavors of NLP models
• (2014) Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors
  • TLDR: Important paper demonstrating that context-predicting distributional semantics approaches outperform count-based techniques.
• (2015) Improving Distributional Similarity with Lessons Learned From Word Embeddings 💡
  • TLDR: Demonstrates that traditional distributional semantics techniques can be enhanced with certain design choices and hyperparameter optimizations that make their performance rival that of neural network-based embedding methods.
• (2018) Universal Language Model Fine-tuning for Text Classification
  • TLDR: Provides a smorgasbord of nice techniques for finetuning language models that can be effectively transferred to text classification tasks.
• (2019) Analogies Explained: Towards Understanding Word Embeddings
  • TLDR: Very nice work providing a mathematical formalism for understanding some of the paraphrasing properties of modern word embeddings.