J0nasW/paperswithcode huggingface dataset:
Task: Automated Theorem Proving
Area: miscellaneous

The goal of **Automated Theorem Proving** is to automatically generate a proof, given a conjecture (the target theorem) and a knowledge base of known facts, all expressed in a formal language. Automated Theorem Proving is useful in a wide range of applications, including the verification and synthesis of software and hardware systems.


<span class="description-source">Source: [Learning to Prove Theorems by Learning to Generate Theorems ](https://arxiv.org/abs/2002.07019)</span>
Task: Self-Organized Clustering
Area: miscellaneous

Clustering with Self-Organized Maps
Task: Ancient Text Restoration
Area: miscellaneous

Image credit: [Restoring and attributing ancient texts using deep neural networks
](https://paperswithcode.com/paper/restoring-and-attributing-ancient-texts-using)
Task: Open Set Learning
Area: miscellaneous

Traditional supervised learning aims to train a classifier in the closed-set world, where training and test samples share the same label space. Open set learning (OSL) is a more challenging and realistic setting, where there exist test samples from the classes that are unseen during training. Open set recognition (OSR) is the sub-task of detecting test samples which do not come from the training.
Task: Causal Inference
Area: miscellaneous

Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

<span style="color:grey; opacity: 0.6">( Image credit: [Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data](https://arxiv.org/pdf/1605.00391v2.pdf) )</span>
Task: Data Summarization
Area: miscellaneous

**Data Summarization** is a central problem in the area of machine learning, where we want to compute a small summary of the data.


<span class="description-source">Source: [How to Solve Fair k-Center in Massive Data Models ](https://arxiv.org/abs/2002.07682)</span>
Task: JSONiq Query Execution
Area: miscellaneous

Execute JSONiq query, typically on semi-structured JSON data
Task: Detect Ground Reflections
Area: miscellaneous

This task helps in detecting the significant ground reflections at mm-wave bands. The harvested ground reflections can help in overcoming transient blockages at mm-wave bands
Task: Outdoor Positioning
Area: miscellaneous

Outdoor Positioning (e.g. GPS)
Task: 3D Bin Packing
Area: miscellaneous

As a classic NP-hard problem, the bin packing problem (1D-BPP) seeks for an assignment of a collection of items with various weights to bins. The optimal assignment houses all the items with the fewest bins such that the total weight of items in a bin is below the bin’s capacity. In its 3D version (3D-BPP), an item has a 3D “weight” corresponding
to its length, width and height.
Task: Music Texture Transfer
Area: music

Texture is the collective temporal homogeneity of acoustic
events.
Task: Music Genre Recognition
Area: music

Recognizing the genre (e.g. rock, pop, jazz, etc.) of a piece of music.
Task: Music Source Separation
Area: music

Music source separation is the task of decomposing music into its constitutive components, e. g., yielding separated stems for the vocals, bass, and drums.

<span style="color:grey; opacity: 0.6">( Image credit: [SigSep](https://github.com/sigsep ) )</span>
Task: Music Modeling
Area: music

<span style="color:grey; opacity: 0.6">( Image credit: [R-Transformer](https://arxiv.org/pdf/1907.05572v1.pdf) )</span>
Task: Audio Super-Resolution
Area: music

AUDIO SUPER-RESOLUTION or speech bandwidth extension (Upsampling Ratio = 2)
Task: Cover song identification
Area: music

**Cover Song Identification** is the task of identifying an alternative version of a previous musical piece, even though it may differ substantially in timbre, tempo, structure, and even fundamental aspects relating to the harmony and melody of the song. The term “cover” is so wide that it ranges from acoustic renditions of a previous song, to Jimi Hendrix’ famous (and radical) reinterpretation of Bob Dylan’s “All Along the Watchtower”, to Rage Against the Machine essentially rewriting Bob Dylan’s “Maggie’s Farm”. Beyond its value for computational musicology and for enhancing music recommendation, Cover Song Identification is of interest because of its potential for benchmarking other music similarity and retrieval algorithms. Chord analysis, melody extraction and music similarity are all strongly connected to Cover Song Identification - another field of music analysis where AI has been applied.


<span class="description-source">Source: [Artificial Musical Intelligence: A Survey ](https://arxiv.org/abs/2006.10553)</span>
Task: Music Generation
Area: music

**Music Generation** is the task of generating music or music-like sounds from a model or algorithm. The goal is to produce a sequence of notes or sound events that are similar to existing music in some way, such as having the same style, genre, or mood.
Task: Music Transcription
Area: music

Music transcription is the task of converting an acoustic musical signal into some form of music notation.

<span style="color:grey; opacity: 0.6">( Image credit: [ISMIR 2015 Tutorial - Automatic Music Transcription](http://c4dm.eecs.qmul.ac.uk/ismir15-amt-tutorial/AMT_tutorial_ISMIR_2015.pdf) )</span>
Task: Fact Verification
Area: natural-language-processing

Fact verification, also called "fact checking", is a process of verifying facts in natural text against a database of facts.
Task: Predicate Detection
Area: natural-language-processing

Detecting predicates in sentences. Semantic frames are defined with respect to predicates. This task is a prerequisite to semantic role labeling.
Task: Hope Speech Detection for Tamil
Area: natural-language-processing

Detecting Hope Speech in the Tamil language
Task: Fake News Detection
Area: natural-language-processing

**Fake News Detection** is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake. The goal of fake news detection is to develop algorithms that can automatically identify and flag fake news articles, which can be used to combat misinformation and promote the dissemination of accurate information.
Task: Causal Emotion Entailment
Area: natural-language-processing

The Causal Emotion Entailment is a simpler version of the span extraction task. In this task, given a
target utterance (U) with emotion E, the goal is to predict which particular utterances in the conversation
history H(U) are responsible for the
emotion E in the target utterance.
Task: Aspect Term Extraction and Sentiment Classification
Area: natural-language-processing

Extracting the aspect terms as well as the corresponding sentiment polarities simultaneously.
Task: Text Attribute Transfer
Area: natural-language-processing

The goal of the **Text Attribute Transfer** task is to change an input text such that the value of a particular linguistic attribute of interest (e.g. language = English, sentiment = Positive) is transferred to a different desired value (e.g. language = French, sentiment = Negative). This task needs approaches that can disentangle the content from other linguistic attributes of the text.


<span class="description-source">Source: [Improved Neural Text Attribute Transfer with Non-parallel Data ](https://arxiv.org/abs/1711.09395)</span>
Task: Hierarchical Text Classification of Blurbs (GermEval 2019)
Area: natural-language-processing

Shared Task on Hierarchical Classification of Blurbs (GermEval 2019 / KONVENS)

https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2019-hmc.html
Task: Lexical Simplification
Area: natural-language-processing

The goal of **Lexical Simplification** is to replace complex words (typically words that are used less often in language and are therefore less familiar to readers) with their simpler synonyms, without infringing the grammaticality and changing the meaning of the text.


<span class="description-source">Source: [Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ](https://arxiv.org/abs/1809.04163)</span>
Task: Text Style Transfer
Area: natural-language-processing

Text Style Transfer is the task of controlling certain attributes of generated text. The state-of-the-art methods can be categorized into two main types which are used on parallel and non-parallel data. Methods on parallel data are typically supervised methods that use a neural sequence-to-sequence model with the encoder-decoder architecture. Methods on non-parallel data are usually unsupervised approaches using Disentanglement, Prototype Editing and Pseudo-Parallel Corpus Construction.

The popular benchmark for this task is  the Yelp Review Dataset. Models are typically evaluated with the metrics of Sentiment Accuracy, BLEU, and PPL.
Task: Text-Variation
Area: natural-language-processing

Generate variations of the input text
Task: True or False Question Answering
Area: natural-language-processing

The objective of the true or false question-answer task is to answer research questions with affirmative, negative, or uncertain responses (e.g., Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?).
Task: Text Classification
Area: natural-language-processing

**Text Classification** is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics. 

Text Classification problems include emotion classification, news classification, citation intent classification, among others. Benchmark datasets for evaluating text classification capabilities include GLUE, AGNews,  among others. 

In recent years, deep learning techniques like XLNet and RoBERTa have attained some of the biggest performance jumps for text classification problems. 

<span style="color:grey; opacity: 0.6">( Image credit: [Text Classification Algorithms: A Survey](https://arxiv.org/pdf/1904.08067v4.pdf) )</span>
Task: Taxonomy Learning
Area: natural-language-processing

Taxonomy learning is the task of hierarchically classifying concepts in an automatic manner from text corpora. The process of building taxonomies is usually divided into two main steps: (1) extracting hypernyms for concepts, which may constitute a field of research in itself (see Hypernym Discovery below) and (2) refining the structure into a taxonomy.

Description from [NLP Progress](http://nlpprogress.com/english/taxonomy_learning.html)
Task: Automated Essay Scoring
Area: natural-language-processing

Essay scoring: **Automated Essay Scoring** is the task of assigning a score to an essay, usually in the context of assessing the language ability of a language learner. The quality of an essay is affected by the following four primary dimensions: topic relevance, organization and coherence, word usage and sentence complexity, and grammar and mechanics.


<span class="description-source">Source: [A Joint Model for Multimodal Document Quality Assessment ](https://arxiv.org/abs/1901.01010)</span>
Task: Irish Text Diacritization
Area: natural-language-processing

Addition of diacritics for undiacritized Irish Wikipedia texts.
Task: Hope Speech Detection for Malayalam
Area: natural-language-processing

Detecting Hopespeech in the Malayalam language
Task: Term Extraction
Area: natural-language-processing

Term Extraction, or Automated Term Extraction (ATE), is about extraction domain-specific terms from natural language text. 
For example, the sentence “We meta-analyzed mortality using random-effect models” contains the domain-specific single-word terms "meta-analyzed", "mortality" and the multi-word term "random-effect models".
Task: Argument Pair Extraction (APE)
Area: natural-language-processing

Argument pair extraction (APE) aims to extract interactive argument pairs from two passages of a discussion.
Task: Text Generation
Area: natural-language-processing

**Text Generation** is the task of generating text with the goal of appearing indistinguishable to human-written text. This task if more formally known as "natural language generation" in the literature.

Text generation can be addressed with Markov processes or deep generative models like LSTMs. Recently, some of the most advanced methods for text generation include [BART](/method/bart), [GPT](/method/gpt) and other [GAN-based approaches](/method/gan). Text generation systems are evaluated either through human ratings or automatic evaluation metrics like METEOR, ROUGE, and BLEU. 

Further readings:

- [The survey: Text generation models in deep learning](https://www.sciencedirect.com/science/article/pii/S1319157820303360)
- [Modern Methods for Text Generation](https://arxiv.org/abs/2009.04968)

<span style="color:grey; opacity: 0.6">( Image credit: [Adversarial Ranking for Language Generation](https://arxiv.org/abs/1705.11001) )</span>
Task: Dialect Identification
Area: natural-language-processing

Dialectal Arabic Identification
Task: Cross-Lingual Natural Language Inference
Area: natural-language-processing

Using data and models available for one language for which ample such resources are available (e.g., English) to solve a natural language inference task in another, commonly more low-resource, language.
Task: Lay Summarization
Area: natural-language-processing

Summarizing a technical or scientific document in simple, non-technical language that is comprehensible to a lay person (non-expert).
Task: Author Attribution
Area: natural-language-processing

Authorship attribution is the task of determining the author of a text.
Task: Mathematical Question Answering
Area: natural-language-processing

Building systems that automatically answer mathematical questions.
Task: Row Annotation
Area: natural-language-processing

**Row Annotation** is the task of linking a row to a real-world entity and is a subtask of [Table Annotation](https://paperswithcode.com/task/table-annotation). It differs from [Cell Entity Annotation](https://paperswithcode.com/task/cell-entity-annotation) because CEA considers the linking of *cells* to entities while Row Annotation assumes that there is only an entity described in a row which usually is located in the main/entity column.

A row is annotated using entities from different knowledge bases such as DBpedia or WikiData.
Task: Variable Disambiguation
Area: natural-language-processing

Identifying which variable is mentioned in a text.
Task: Page Stream Segmentation
Area: natural-language-processing

page stream segmentation (PSS) is the task to automatically separate a stream of scanned images into coherent multi-page documents
Task: Toxic Spans Detection
Area: natural-language-processing

Given a sentence identify the toxic spans present in it.
Task: Dialogue Safety Prediction
Area: natural-language-processing

Determine the safety of a given dialogue context.
Task: Morpheme Segmentaiton
Area: natural-language-processing

Succesful systems segment a given word or sentence into a sequence of morphemes.
Task: Text-Based Stock Prediction
Area: natural-language-processing

Make stock predictions based on text (e.g., news articles, twitters, etc.).
Task: Text Clustering
Area: natural-language-processing

Grouping a set of texts in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). (Source: Adapted from Wikipedia)
Task: Constituency Grammar Induction
Area: natural-language-processing

Inducing a constituency-based phrase structure grammar.
Task: User Simulation
Area: natural-language-processing

Simulation of user interacting with a dialog system
Task: Knowledge Base Question Answering
Area: natural-language-processing

Knowledge Base Q&A is the task of answering questions from a knowledge base.

<span style="color:grey; opacity: 0.6">( Image credit: [Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering](https://www.aclweb.org/anthology/C18-1280.pdf) )</span>
Task: Grammatical Error Detection
Area: natural-language-processing

Grammatical Error Detection (GED) is the task of detecting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors. Grammatical error detection (GED) is one of the key component in grammatical error correction (GEC) community.
Task: Zero-shot Relation Triplet Extraction
Area: natural-language-processing

Given an input sentence, the task is to extract triplets consisting of the head entity, relation label, and tail entity where the relation label is not seen at the training stage.
Task: KB-to-Language Generation
Area: natural-language-processing

Given information from a knowledge base, generate a description of this information in natural language.
Task: Croatian Text Diacritization
Area: natural-language-processing

Addition of diacritics for undiacritized Croatian Wikipedia texts.
Task: Grammatical Error Correction
Area: natural-language-processing

Grammatical Error Correction (GEC) is the task of correcting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors. 

GEC is typically formulated as a sentence correction task. A GEC system takes a potentially erroneous sentence as input and is expected to transform it to its corrected version. See the example given below: 

| Input (Erroneous)          | Output (Corrected)     |
| -------------------------  | ---------------------- |
|She see Tom is catched by policeman in park at last night. | She saw Tom caught by a policeman in the park last night.|
Task: Twitter Sentiment Analysis
Area: natural-language-processing

Twitter sentiment analysis is the task of performing sentiment analysis on tweets from Twitter.
Task: Text-to-Video Generation
Area: natural-language-processing

This task refers to video generation based on a given sentence or sequence of words.
Task: Role-filler Entity Extraction
Area: natural-language-processing

Role-filler entity extraction task on the MUC-4 dataset.
Task: Bilingual Lexicon Induction
Area: natural-language-processing

Translate words from one language to another.
Task: Dialog Relation Extraction
Area: natural-language-processing

Dialog Relation Extraction is the task of predicting the relation type between entities mentioned in dialogue. It uses multiple tokens to capture possible relations between pairs of entities in the dialogue. The popular benchmark for this task is the DialogRE dataset. The models are typically evaluated with the metric of F1 Score for both standard-setting and conversational settings.
Task: Cross-Lingual
Area: natural-language-processing

Cross-lingual natural language processing is the task of using data and models available for one language for which ample such resources are available (e.g., English) to solve tasks in another, commonly more low-resource, language.
Task: Natural Language Landmark Navigation Instructions Generation
Area: natural-language-processing

Generate natural language navigation instructions that revolve around visual landmarks instead of turn-by-turn directives.
Task: Phrase Tagging
Area: natural-language-processing

A fine-grained task that aims to find all occurrences of phrases in sentences.
Task: Cross-Language Text Summarization
Area: natural-language-processing

Using data and models available for one language for which ample such resources are available (e.g., English) to solve summarization tasks in another, commonly more low-resource, language.
Task: Visual Storytelling
Area: natural-language-processing

<span style="color:grey; opacity: 0.6">( Image credit: [No Metrics Are Perfect](https://github.com/eric-xw/AREL) )</span>
Task: Multimodal Abstractive Text Summarization
Area: natural-language-processing

Abstractive text summarization by utilizing information from multiple modalities.
Task: French Text Diacritization
Area: natural-language-processing

Addition of diacritics for undiacritized French Wikipedia texts.
Task: Question Answering
Area: natural-language-processing

**Question Answering** is the task of answering questions (typically reading comprehension questions), but abstaining when presented with a question that cannot be answered based on the provided context. 

Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include [SQuAD](/dataset/squad), [HotPotQA](/dataset/hotpotqa), [bAbI](/dataset/babi-1), [TriviaQA](/dataset/triviaqa), [WikiQA](/dataset/wikiqa), and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.

( Image credit: [SQuAD](https://rajpurkar.github.io/mlx/qa-and-squad/) )
Task: Tweet-Reply Sentiment Analysis
Area: natural-language-processing

To predict the predominant sentiment among (potential) first-order replies to a given tweet, in a Message-level Polarity Classification paradigm.
Task: Record linking
Area: natural-language-processing

The task of finding records in a data set that refer to the same entity across different data sources. 

Record linking is also called *entity resolution* or *entity matching*. Further material about this task is collected at [entity resolution](https://paperswithcode.com/task/entity-resolution).
Task: Multimodal Lexical Translation
Area: natural-language-processing

Translate a given word in a source language to a word in the target language, given the source sentence and one or more images illustrating the word.
Task: Ad-Hoc Information Retrieval
Area: natural-language-processing

Ad-hoc information retrieval refers to the task of returning information resources related to a user query formulated in natural language.
Task: Natural Language Inference
Area: natural-language-processing

**Natural language inference (NLI)** is the task of determining whether a "hypothesis" is 
true (entailment), false (contradiction), or undetermined (neutral) given a "premise".

Example:

| Premise | Label | Hypothesis |
| --- | ---| --- |
| A man inspects the uniform of a figure in some East Asian country. | contradiction | The man is sleeping. |
| An older and younger man smiling. | neutral  | Two men are smiling and laughing at the cats playing on the floor. |
| A soccer game with multiple males playing. | entailment | Some men are playing a sport. |

Approaches used for NLI include earlier symbolic and statistical approaches to more recent deep learning approaches. Benchmark datasets used for NLI include [SNLI](/dataset/snli), [MultiNLI](/dataset/multinli), [SciTail](/dataset/scitail), among others. You can get hands-on practice on the SNLI task by following this [d2l.ai chapter](https://d2l.ai/chapter_natural-language-processing-applications/natural-language-inference-and-dataset.html).

Further readings:

- [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172)
Task: Hate Speech Detection
Area: natural-language-processing

Hate speech detection is the task of detecting if communication such as text, audio, and so on contains hatred and or encourages violence towards a person or a group of people. This is usually based on prejudice against 'protected characteristics' such as their ethnicity, gender, sexual orientation, religion, age et al. Some example benchmarks are ETHOS and HateXplain. Models can be evaluated with metrics like the F-score or F-measure.
Task: Conditional Text Generation
Area: natural-language-processing

The task of generating text according to some pre-specified conditioning (e.g. topic or sentiment or constraint)
Task: Query Wellformedness
Area: natural-language-processing

Assessing whether a query is grammatically correct, contains no spelling mistakes, and asks an explicit question.

Image Source: [Identifying Well-formed Natural Language Questions](https://arxiv.org/pdf/1808.09419.pdf)