J0nasW/paperswithcode huggingface dataset:
Task: Reading Comprehension
Area: natural-language-processing

Most current question answering datasets frame the task as reading comprehension where the question is about a paragraph or document and the answer often is a span in the document. 

Some specific tasks of reading comprehension include multi-modal machine reading comprehension and textual machine reading comprehension, among others. In the literature, machine reading comprehension can be divide into four categories: **cloze style**, **multiple choice**, **span prediction**, and **free-form answer**.  Read more about each category [here](https://paperswithcode.com/paper/a-survey-on-machine-reading-comprehension-1).

Benchmark datasets used for testing a model's reading comprehension abilities include [MovieQA](/dataset/movieqa), [ReCoRD](dataset/record), and [RACE](/dataset/race), among others.

The Machine Reading group at UCL also provides an [overview of reading comprehension tasks](https://uclnlp.github.io/ai4exams/data.html).

Figure source: [A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets](https://arxiv.org/pdf/2006.11880.pdf)
Task: Information Retrieval
Area: natural-language-processing

Information retrieval is the task of ranking a list of documents or search results in response to a query

<span style="color:grey; opacity: 0.6">( Image credit: [sudhanshumittal](https://github.com/sudhanshumittal/Information-retrieval-system) )</span>
Task: Spanish Text Diacritization
Area: natural-language-processing

Addition of diacritics for undiacritized Spanish Wikipedia texts.
Task: Workflow Discovery
Area: natural-language-processing

Workflow Discovery (WD) was introduced by [Workflow Discovery from Dialogues in the Low Data Regime](https://openreview.net/forum?id=L9othQvPks). WD aims to extract work-flows that have either implicitly or explicitly guided task-oriented dialogues be-
tween two people. Workflows extracted from a conversation consist of a summary of the key actions taken during
the dialogue. These workflows consist of pre-defined terms for actions and slots when possible, but our approach also al-
lows for actions that are not known to be invented by the model online and used as new steps in the generated work-
flow.
Task: Clickbait Detection
Area: natural-language-processing

Clickbait detection is the task of identifying clickbait, a form of false advertisement, that uses hyperlink text or a thumbnail link that is designed to attract attention and to entice users to follow that link and read, view, or listen to the linked piece of online content, with a defining characteristic of being deceptive, typically sensationalized or misleading (Source: Adapted from Wikipedia)
Task: Column Type Annotation
Area: natural-language-processing

**Column type annotation** (CTA) refers to the task of predicting the semantic type of a table column and is a subtask of [Table Annotation](https://paperswithcode.com/task/table-annotation). The labels that are usually used in a CTA problem are semantic types from vocabularies like DBpedia, Schema.org or WikiData. Some examples are: *Book*, *Country*, *LocalBusiness* etc.

CTA can be either treated as a multi-class classification problem where a column is annotated by only one semantic type or as multi-label classification problem where a column can be annotated using multiple semantic types.
Task: Zero-Shot Machine Translation
Area: natural-language-processing

Translate text or speech from one language to another without supervision.
Task: Contextualized Literature-based Discovery
Area: natural-language-processing

Given a seed term (e.g., a task or method in NLP, or a disease in biomedicine) and corresponding background (e.g., challenges for a given task), the model's aim is to generate idea suggestions.
The Contextual Literature-Based Discovery (CLBD) will take two different formulations of C-LBD: idea sentence generation and idea node prediction.
Task: Fine-Grained Opinion Analysis
Area: natural-language-processing

Fine-Grained Opinion Analysis aims to: (i) detect opinion expressions that convey attitudes such as sentiments, agreements, beliefs, or intentions, (ii) measure their intensity, (iii) identify their holders i.e. entities that express an attitude, (iv) identify their targets i.e. entities or propositions at which the attitude is directed, and (v) classify their target-dependent attitude.

<span style="color:grey; opacity: 0.6">( Image credit: [SRL4ORL](https://arxiv.org/pdf/1711.00768v3.pdf) )</span>
Task: Text Categorization
Area: natural-language-processing

**Text Categorization** is the task of automatically assigning pre-defined categories to documents written in natural languages. Several types of Text Categorization have been studied, each of which deals with different types of documents and categories, such as topic categorization to detect discussed topics (e.g., sports, politics), spam detection, and sentiment classification to determine the sentiment typically in product or movie reviews.


<span class="description-source">Source: [Effective Use of Word Order for Text Categorization with Convolutional Neural Networks ](https://arxiv.org/abs/1412.1058)</span>
Task: Long-range modeling
Area: natural-language-processing

A new task for testing the long-sequence modeling capabilities and efficiency of language models.

Image credit: [SCROLLS: Standardized CompaRison Over Long Language Sequences](https://arxiv.org/pdf/2201.03533v1.pdf)
Task: Face Selection
Area: natural-language-processing

A task where an agent should select at most two sentences from the paper as argumentative facts.
Task: Event Relation Extraction
Area: natural-language-processing

To extract relations among events, such as event coreference, temporal, causal and subevent relations.
Task: Counterspeech Detection
Area: natural-language-processing

Counter-speech detection is the task of detecting counter-speech, i.e., a crowd-sourced response that argues, disagrees, or presents an opposing view to extremism or hateful content on social media platforms (Source: Adapted from: https://icsr.info/wp-content/uploads/2018/03/ICSR-Report-Challenging-Hate-Counter-speech-Practices-in-Europe.pdf)
Task: Semantic Parsing
Area: natural-language-processing

**Semantic Parsing** is the task of transducing natural language utterances into formal meaning representations. The target meaning representations can be defined according to a wide variety of formalisms. This include linguistically-motivated semantic representations that are designed to capture the meaning of any sentence such as λ-calculus or the abstract meaning representations. Alternatively, for more task-driven approaches to Semantic Parsing, it is common for meaning representations to represent executable programs such as SQL queries, robotic commands, smart phone instructions, and even general-purpose programming languages like Python and Java.


<span class="description-source">Source: [Tranx: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation ](https://arxiv.org/abs/1810.02720)</span>
Task: Table Type Detection
Area: natural-language-processing

Table Type Detection is the task of predicting the semantic type of a table and is a subtask of [Table Annotation](https://paperswithcode.com/task/table-annotation).  The labels used for annotation in this task are types from vocabularies like DBpedia, Schema.org etc. like *Music* or *Hotel*. The semantic type of a table can indicate the content of a table. For example, if the semantic type of a table is *Music* it indicates that the table consists of music records/entities.
Task: Controllable Language Modelling
Area: natural-language-processing

Given unsupervised Language Modeling as pretraining task, the objective is to generate texts under particular control attributes (Topic, Sentiment)
Task: Simultaneous Speech-to-Text Translation
Area: natural-language-processing

Simultaneous Speech-to-Text translation aims to translate concurrently with the source speech.  It is crucial since it enables real-time interpretation of conversations, lectures and talks.
Task: Intent Discovery
Area: natural-language-processing

Given a set of labelled and unlabelled utterances, the idea is to identify existing (known) intents and potential (new intents) intents. This method can be utilised in conversational system setting.
Task: Low-Resource Neural Machine Translation
Area: natural-language-processing

Low-resource machine translation is the task of machine translation on a low-resource language where large data may not be available.
Task: Cell Entity Annotation
Area: natural-language-processing

**Cell Entity Annotation** (CEA) is the task of annotating cells in a table with an entity from a knowledge base and is a subtask of [Table Annotation](https://paperswithcode.com/task/table-annotation). CEA problem labels are entities from knowledge bases such as DBpedia or WikiData. It usually is considered as a multi-class classification problem.

CEA can also be referred to in different works as the problem of entity linking, as it links a cell in a table to an entity.
Task: Semantic Similarity
Area: natural-language-processing

The main objective **Semantic Similarity** is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are knowledge-based approaches and corpus-based, distributional methods.


<span class="description-source">Source: [Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection ](https://arxiv.org/abs/1801.03145)</span>
Task: Coreference Resolution
Area: natural-language-processing

Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities.

Example:

```
               +-----------+
               |           |
I voted for Obama because he was most aligned with my values", she said.
 |                                                 |            |
 +-------------------------------------------------+------------+
```

"I", "my", and "she" belong to the same cluster and "Obama" and "he" belong to the same cluster.
Task: Persian Sentiment Analysis
Area: natural-language-processing

Persian Sentiment analysis is the task of classifying the polarity of a given text.
Task: Variable Detection
Area: natural-language-processing

Identifying whether a sentence contains a variable mention.
Task: Language Identification
Area: natural-language-processing

Language identification is the task of determining the language of a text.
Task: Zero-Shot Text-to-Image Generation
Area: natural-language-processing

Image credit: [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://paperswithcode.com/paper/glide-towards-photorealistic-image-generation)
Task: Citation Intent Classification
Area: natural-language-processing

Identifying the reason why an author cited another author.
Task: Privacy Preserving Deep Learning
Area: natural-language-processing

The goal of privacy-preserving (deep) learning is to train a model while preserving privacy of the training dataset. Typically, it is understood that the trained model should be privacy-preserving (e.g., due to the training algorithm being differentially private).
Task: Semi-Supervised Formality Style Transfer
Area: natural-language-processing

Semi-Supervised Formality Style Transfer
Task: Lexical Analysis
Area: natural-language-processing

Lexical analysis is the process of converting a sequence of characters into a sequence of tokens (strings with an assigned and thus identified meaning). (Source: Adapted from Wikipedia)
Task: Multimodal Deep Learning
Area: natural-language-processing

**Multimodal deep learning** is a type of deep learning that combines information from multiple modalities, such as text, image, audio, and video, to make more accurate and comprehensive predictions. It involves training deep neural networks on data that includes multiple types of information and using the network to make predictions based on this combined data.

One of the key challenges in multimodal deep learning is how to effectively combine information from multiple modalities. This can be done using a variety of techniques, such as fusing the features extracted from each modality, or using attention mechanisms to weight the contribution of each modality based on its importance for the task at hand.

Multimodal deep learning has many applications, including image captioning, speech recognition, natural language processing, and autonomous vehicles. By combining information from multiple modalities, multimodal deep learning can improve the accuracy and robustness of models, enabling them to perform better in real-world scenarios where multiple types of information are present.
Task: Dialogue Act Classification
Area: natural-language-processing

Dialogue act classification is the task of classifying an utterance with respect to the function it serves in a dialogue, i.e. the act the speaker is performing. Dialogue acts are a type of speech acts (for Speech Act Theory, see [Austin (1975)](http://www.hup.harvard.edu/catalog.php?isbn=9780674411524) and [Searle (1969)](https://www.cambridge.org/core/books/speech-acts/D2D7B03E472C8A390ED60B86E08640E7)).
Task: Few-Shot Relation Classification
Area: natural-language-processing

**Few-Shot Relation Classification** is a particular relation classification task under minimum annotated data, where a model is required to classify a new incoming query instance given only few support instances (e.g., 1 or 5) during testing.


<span class="description-source">Source: [MICK: A Meta-Learning Framework for Few-shot Relation Classification with Little Training Data ](https://arxiv.org/abs/2004.14164)</span>
Task: Timeline Summarization
Area: natural-language-processing

Identifying key dates of major events and providing short descriptions of what happened on these dates. (Source: https://www.aclweb.org/anthology/D19-5403/)
Task: Interactive Evaluation of Dialog
Area: natural-language-processing

Task that involve building/adapting conversation models to work effectively in an interactive setting.
Task: Scientific Results Extraction
Area: natural-language-processing

Scientific results extraction is the task of extracting relevant result information (e.g., in the case of Machine learning performance results: task, dataset, metric name, metric value) from the scientific literature.
Task: Coding Problem Tagging
Area: natural-language-processing

Assigning data structures to coding problems
Task: Code Documentation Generation
Area: natural-language-processing

Code Documentation Generation is a supervised task where a code function is the input to the model, and the model generates the documentation for this function.

Description from: [CodeTrans: Towards Cracking the Language of Silicone's Code Through Self-Supervised Deep Learning and High Performance Computing](https://arxiv.org/pdf/2104.02443.pdf)
Task: Profile Generation
Area: natural-language-processing

**Profile Generation** is the task of creating a profile for an individual or entity based on information about them. The goal of profile generation is to gather as much relevant information about the individual or entity as possible, in order to build an accurate and comprehensive profile.
Task: Natural Language Understanding
Area: natural-language-processing

**Natural Language Understanding** is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension. Applications enabled by natural language understanding range from question answering  to automated reasoning.



<span class="description-source">Source: [Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test? ](https://arxiv.org/abs/1812.05411)</span>
Task: Latvian Text Diacritization
Area: natural-language-processing

Addition of diacritics for undiacritized Latvian Wikipedia texts.
Task: Temporal Information Extraction
Area: natural-language-processing

Temporal information extraction is the identification of chunks/tokens corresponding to temporal intervals, and the extraction and determination of the temporal relations between those. The entities extracted may be temporal expressions (timexes), eventualities (events), or auxiliary signals that support the interpretation of an entity or relation. Relations may be temporal links (tlinks), describing the order of events and times, or subordinate links (slinks) describing modality and other subordinative activity, or aspectual links (alinks) around the various influences aspectuality has on event structure.

The markup scheme used for temporal information extraction is well-described in the ISO-TimeML standard, and also on [www.timeml.org](http://www.timeml.org).

```
<?xml version="1.0" ?>

<TimeML xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="http://timeml.org/timeMLdocs/TimeML_1.2.1.xsd">
<TEXT>


 PRI20001020.2000.0127 
 NEWS STORY 
 <TIMEX3 tid="t0" type="TIME" value="2000-10-20T20:02:07.85">10/20/2000 20:02:07.85</TIMEX3> 


 The Navy has changed its account of the attack on the USS Cole in Yemen.
 Officials <TIMEX3 tid="t1" type="DATE" value="PRESENT_REF" temporalFunction="true" anchorTimeID="t0">now</TIMEX3> say the ship was hit <TIMEX3 tid="t2" type="DURATION" value="PT2H">nearly two hours </TIMEX3>after it had docked.
 Initially the Navy said the explosion occurred while several boats were helping
 the ship to tie up. The change raises new questions about how the attackers
 were able to get past the Navy security.


 <TIMEX3 tid="t3" type="TIME" value="2000-10-20T20:02:28.05">10/20/2000 20:02:28.05</TIMEX3> 



<TLINK timeID="t2" relatedToTime="t0" relType="BEFORE"/>
</TEXT>
</TimeML>
```

To avoid leaking knowledge about temporal structure, train, dev and test splits must be made at document level for temporal information extraction.
Task: Cross-Lingual Transfer
Area: natural-language-processing

Cross-lingual transfer refers to transfer learning 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: Reader-Aware Summarization
Area: natural-language-processing

Using reader comments to improve summarization performance.
Task: News Generation
Area: natural-language-processing

Generation of larger segments of text with consistent topic and evolving story.
Task: PICO
Area: natural-language-processing

The proliferation of healthcare data has contributed to the widespread usage of the PICO paradigm for creating specific clinical questions from RCT. 

PICO is a mnemonic that stands for:

Population/Problem: Addresses the characteristics of populations involved and the specific characteristics of the disease or disorder.
Intervention: Addresses the primary intervention (including treatments, procedures, or diagnostic tests) along with any risk factors.
Comparison: Compares the efficacy of any new interventions with the primary intervention.
Outcome: Measures the results of the intervention, including improvements or side effects.
PICO is an essential tool that aids evidence-based practitioners in creating precise clinical questions and searchable keywords to address those issues. It calls for a high level of technical competence and medical domain knowledge, but it’s also frequently very time-consuming.

Automatically identifying PICO elements from this large sea of data can be made easier with the aid of machine learning (ML) and natural language processing (NLP). This facilitates the development of precise research questions by evidence-based practitioners more quickly and precisely.

Empirical studies have shown that the use of PICO frames improves the specificity and conceptual clarity of clinical problems, elicits more information during pre-search reference interviews, leads to more complex search strategies, and yields more precise search results.
Task: Morphological Inflection
Area: natural-language-processing

**Morphological Inflection** is the task of generating a target (inflected form) word from a source word (base form), given a morphological attribute, e.g. number, tense, and person etc. It is useful for alleviating data sparsity issues in translating morphologically rich languages. The transformation from a base form to an inflected form usually includes concatenating the base form with a prefix or a suffix and substituting some characters. For example, the inflected form of a Finnish stem eläkeikä (retirement age) is eläkeiittä when the case is abessive and the number is plural.


<span class="description-source">Source: [Tackling Sequence to Sequence Mapping Problems with Neural Networks ](https://arxiv.org/abs/1810.10802)</span>
Task: Literature Mining
Area: natural-language-processing

The task where the publication texts are used to mine knowledge using NLP
Task: Question Generation
Area: natural-language-processing

The goal of **Question Generation** is to generate a valid and fluent question according to a given passage and the target answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chatbots to lead a conversation. 

<span class="description-source">Source: [Generating Highly Relevant Questions ](https://arxiv.org/abs/1910.03401)</span>
Task: Handwritten Chinese Text Recognition
Area: natural-language-processing

Handwritten Chinese text recognition is the task of interpreting handwritten Chinese input, e.g., from images of documents or scans.
Task: Coherence Evaluation
Area: natural-language-processing

Evaluating the overall coherence of text as measured by its readability and flow through ideas.
Task: Bangla Spelling Error Correction
Area: natural-language-processing

Bangla spell checker which improves the quality of suggestions for misspelled words.
Task: Fact-based Text Editing
Area: natural-language-processing

Fact-based Text Editing aims to revise a given document to better describe the facts in a knowledge base (e.g., several triples).
Task: Meeting Summarization
Area: natural-language-processing

Generating a summary from meeting transcriptions.   
A survey for this task: [Abstractive Meeting Summarization: A Survey](https://paperswithcode.com/paper/abstractive-meeting-summarization-a-survey)
Task: Linguistic Acceptability
Area: natural-language-processing

Linguistic Acceptability is the task of determining whether a sentence is grammatical or ungrammatical.

Image Source: [Warstadt et al](https://arxiv.org/pdf/1901.03438v4.pdf)
Task: Extractive Tags Summarization
Area: natural-language-processing

The goal of Extractive Tags Summarization (ETS) task is to shorten the list of tags corresponding to a digital image while keeping the representativity; i.e., is to extract important tags from the context lying in an image and its corresponding tags.
Task: Chinese Named Entity Recognition
Area: natural-language-processing

Chinese named entity recognition is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. from Chinese text (Source: Adapted from Wikipedia).
Task: Implicit Discourse Relation Classification
Area: natural-language-processing

Parsing a text into a set of discourse relations between two adjacent or non-adjacent discourse units in the absence of explicit connectives, such as 'but' or 'however', and classifying those relations. (Source: Adapted from https://www.cs.brandeis.edu/~clp/conll15st/intro.html)
Task: Speculation Scope Resolution
Area: natural-language-processing

Identifiy the scope of a speculation cue that indicates uncertainty in a given text.