J0nasW/paperswithcode huggingface dataset:
Task: Question to Declarative Sentence
Area: natural-language-processing

Question Answer to Declarative Sentence (QA2D) is the task of generating declarative statements from question, answer pairs.

See:
Demszky, D., Guu, K., & Liang, P. (2018). Transforming Question Answering Datasets Into Natural Language Inference Datasets. arXiv preprint. arXiv:1809.02922
Task: XLM-R
Area: natural-language-processing

XLM-R
Task: multimodal generation
Area: natural-language-processing

**Multimodal generation** refers to the process of generating outputs that incorporate multiple modalities, such as images, text, and sound. This can be done using deep learning models that are trained on data that includes multiple modalities, allowing the models to generate output that is informed by more than one type of data.

For example, a multimodal generation model could be trained to generate captions for images that incorporate both text and visual information. The model could learn to identify objects in the image and generate descriptions of them in natural language, while also taking into account contextual information and the relationships between the objects in the image.

Multimodal generation can also be used in other applications, such as generating realistic images from textual descriptions or generating audio descriptions of video content. By combining multiple modalities in this way, multimodal generation models can produce more accurate and comprehensive output, making them useful for a wide range of applications.
Task: Multi-Grained Named Entity Recognition
Area: natural-language-processing

Multi-Grained Named Entity Recognition aims to detect and recognize entities on multiple granularities, without explicitly assuming non-overlapping or totally nested structures.
Task: Subjectivity Analysis
Area: natural-language-processing

A related task to sentiment analysis is the subjectivity analysis with the goal of labeling an opinion as either subjective or objective.
Task: Humor Detection
Area: natural-language-processing

Humor detection is the task of identifying comical or amusing elements.
Task: KG-to-Text Generation
Area: natural-language-processing

Knowledge-graph-to-text (KG-to-text) generation aims to generate high-quality texts which are consistent with input graphs.

Description from: [JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs](https://arxiv.org/pdf/2106.10502v1.pdf)
Task: Intent Detection
Area: natural-language-processing

**Intent Detection** is a vital component of any task-oriented conversational system. In order to understand the user’s current goal, the system must leverage its intent detector to classify the user’s utterance (provided in varied natural language) into one of several predefined classes, that is, intents.  However, the performance of intent detection has been hindered by the data scarcity issue, as it is non-trivial to collect sufficient examples for new intents. How to effectively identify user intents in few-shot learning has become popular. 


<span class="description-source">Source: [Few-shot Intent Detection Datasets, Baselines and Results ](https://github.com/jianguoz/Few-Shot-Intent-Detection)</span>
<span class="description-source">

<span class="description-source">Source: [Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection ](https://github.com/jianguoz/Few-Shot-Intent-Detection)</span>
<span class="description-source">

Source: [Efficient Intent Detection with Dual Sentence Encoders](https://github.com/PolyAI-LDN/polyai-models)</span>
Task: Unsupervised Part-Of-Speech Tagging
Area: natural-language-processing

Marking up a word in a text (corpus) as corresponding to a particular part of speech based on both its definition and its context by using an untagged corpus for training and producing the tagset by induction (Source: Wikipedia).
Task: Semantic Role Labeling (predicted predicates)
Area: natural-language-processing

PropBank semantic role labeling with predicted predicates.
Task: Speculation Detection
Area: natural-language-processing

Identifying information in text that is speculative as opposed to factual information.
Task: Summarization
Area: natural-language-processing

Summarization is the task of producing a shorter version of one or several documents that preserves most of the
input's meaning.
Task: image-sentence alignment
Area: natural-language-processing

Predict the alignment (score) between an image and a sentence.
Task: Visual Dialog
Area: natural-language-processing

Visual Dialog requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a follow-up question about the image, the task is to answer the question.
Task: Chinese
Area: natural-language-processing

Chinese language processing is the task of applying natural language processing to the Chinese language.
Task: Grounded language learning
Area: natural-language-processing

Acquire the meaning of language in situated environments.
Task: Czech Text Diacritization
Area: natural-language-processing

Addition of diacritics for undiacritized Czech Wikipedia texts.
Task: Scientific Concept Extraction
Area: natural-language-processing

Identification of scientific concepts in research articles.
Task: Temporal Tagging
Area: natural-language-processing

Identification of the extent of a temporal expression (timex) in a text. The temporal expressions can be explicit (e.g. "October, 27 ") or implicit (e.g. "last month"). 

Other names: Timex Extraction; Timex Identification; Timex Detection
Task: CCG Supertagging
Area: natural-language-processing

Combinatory Categorical Grammar (CCG; [Steedman, 2000](http://www.citeulike.org/group/14833/article/8971002)) is a
highly lexicalized formalism. The standard parsing model of [Clark and Curran (2007)](https://www.mitpressjournals.org/doi/abs/10.1162/coli.2007.33.4.493)
uses over 400 lexical categories (or _supertags_), compared to about 50 part-of-speech tags for typical parsers.

Example:

| Vinken | , | 61 | years | old |
| --- | ---| --- | --- | --- |
| N| , | N/N | N | (S[adj]\ NP)\ NP |
Task: Meme Captioning
Area: natural-language-processing

Automatic generation of natural language descriptions of the content of an input meme.
Task: Story Completion
Area: natural-language-processing

Given a story prefix and two possible endings, determining which one is the correct (coherent) ending of the story.
Task: Logical Reasoning Reading Comprehension
Area: natural-language-processing

Logical reasoning reading comprehension is a task proposed by the paper ReClor (ICLR 2020),  which is to evaluate the logical reasoning ability of machine reading comprehension models. ReClor is the first dataset for logical reasoning reading comprehension.
Task: Chatbot
Area: natural-language-processing

**Chatbot** or conversational AI is a language model designed and implemented to have conversations with humans. 


<span class="description-source">Source: [Open Data Chatbot ](https://arxiv.org/abs/1909.03653)</span>

[Image source](https://arxiv.org/pdf/2006.16779v3.pdf)
Task: Continual Relation Extraction
Area: natural-language-processing

Compared with traditional relation extraction, CRE aims to help the model learn new relations while maintaining accurate classification of old ones.
Task: Continual Named Entity Recognition
Area: natural-language-processing

Continual learning for named entity recogntion
Task: Anaphora Resolution
Area: natural-language-processing

Resolving what expression a pronoun or a noun phrase refers to.
Task: Fact Selection
Area: natural-language-processing

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

Given a short text, finding an appropriate response (Source: http://staff.ustc.edu.cn/~cheneh/paper_pdf/2013/HaoWang.pdf)
Task: Open-Domain Question Answering
Area: natural-language-processing

Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.
Task: Goal-Oriented Dialogue Systems
Area: natural-language-processing

Achieving a pre-defined goal through a dialog.
Task: Sketch-to-text Generation
Area: natural-language-processing

Generate a full text based on a sketch (key information consisting of textual spans, phrases, or words), where the sketch may only make up a very small part of the full text.
Task: Cross-Lingual Information Retrieval
Area: natural-language-processing

Cross-Lingual Information Retrieval (CLIR) is a retrieval task in which search queries and candidate documents are written in different languages. CLIR can be very useful in some scenarios. For example, a reporter may want to search foreign language news to obtain different perspectives for her story; an inventor may explore the patents in another country to understand prior art.
Task: Dialogue Interpretation
Area: natural-language-processing

Interpreting the meaning of a dialog.
Task: Semantic Dependency Parsing
Area: natural-language-processing

Identify semantic relationships between words in a text using a graph representation.
Task: Columns Property Annotation
Area: natural-language-processing

**Column Property Annotation** (CPA) refers to the task of predicting the semantic relation between two columns and is a subtask of [Table Annotation](https://paperswithcode.com/task/table-annotation). The input of a CPA problem is most commonly a pair of columns, but can also be only one column. The labels used in CPA are properties from vocabularies. Some examples are *name*, *price*, *datePublished* etc.

CPA is usually a multi-class classification problem and is also referred to as column relation annotation or relation extraction in different works.
Task: Chemical Indexing
Area: natural-language-processing

Predict which chemicals should be indexed.
Task: Question Similarity
Area: natural-language-processing

This is the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions
Task: Entity Resolution
Area: natural-language-processing

**Entity resolution** (also known as entity matching, record linkage, or duplicate detection) is the task of finding records that refer to the same real-world entity across different data sources (e.g., data files, books, websites, and databases). (Source: Wikipedia)

Surveys on entity resolution:

- [Vassilis et al.: End-to-End Entity Resolution for Big Data: A Survey](https://arxiv.org/pdf/1905.06397.pdf), 2020.

- [Barlaug and Gulla: Neural Networks for Entity Matching: A Survey](https://arxiv.org/pdf/2010.11075.pdf), 2021.

The task of entity resolution is closely related to the task of [entity alignment](https://paperswithcode.com/task/entity-alignment) which focuses on matching entities between knowledge bases. The task of [entity linking](https://paperswithcode.com/task/entity-linking) differs from entity resolution as entity linking focuses on identifying entity mentions in free text.
Task: Abstract Anaphora Resolution
Area: natural-language-processing

Abstract Anaphora Resolution aims to resolve nominal expressions (e.g., this result, those two actions) and pronominal expressions (e.g. this, that, it) that refer to abstract-object-antecedents such as facts, events, plans, actions, or situations.
Task: Sentence Pair Modeling
Area: natural-language-processing

Comparing two sentences and their relationship based on their internal representation.
Task: Multimedia Generative Script Learning
Area: natural-language-processing

Given an activity goal $G$, an optional subgoal $M$ that specifies the concrete needs, and the previous multimedia step history $H_n=\{(S_1,V_1),...,(S_n,V_n)\}$ with length $n$, a model is expected to predict the next possible step $S_{n+1}$, where $S_i$ is a text sequence and $V_i$ is an image.
Task: Hypernym Discovery
Area: natural-language-processing

Given a corpus and a target term (hyponym), the task of hypernym discovery consists of extracting a set of its most appropriate hypernyms from the corpus. For example, for the input word “dog”, some valid hypernyms would be “canine”, “mammal” or “animal”.
Task: Textual Analogy Parsing
Area: natural-language-processing

Textual Analogy Parsing (TAP) is the task of identifying analogy frames from text.

<span style="color:grey; opacity: 0.6">( Image credit: [Textual Analogy Parsing: What’s Shared and
What’s Compared among Analogous Facts](https://arxiv.org/pdf/1809.02700v1.pdf) )</span>
Task: Component Classification
Area: natural-language-processing

Classification of argumentative components inside a document
Task: News Annotation
Area: natural-language-processing

Assigning the appropriate labels to a news text based on a set of pre-defined labels.
Task: NER
Area: natural-language-processing

The named entity recognition (NER) involves identification of key information in the text and classification into a set of predefined categories. This includes standard entities in the text like Part of Speech (PoS) and entities like places, names etc...
Task: Keyphrase Extraction
Area: natural-language-processing

A classic task to extract salient phrases that best summarize a document, which essentially has two stages: candidate generation and keyphrase ranking.
Task: DRS Parsing
Area: natural-language-processing

Discourse Representation Structures (DRS) are formal meaning representations introduced by Discourse Representation Theory. DRS parsing is a complex task, comprising other NLP tasks, such as semantic role labeling, word sense disambiguation, co-reference resolution and named entity tagging. Also, DRSs show explicit scope for certain operators, which allows for a more principled and linguistically motivated treatment of negation, modals and quantification, as has been advocated in formal semantics. Moreover, DRSs can be translated to formal logic, which allows for automatic forms of inference by third parties.

Description from [NLP Progress](http://nlpprogress.com/english/semantic_parsing.html)
Task: Personalized and Emotional Conversation
Area: natural-language-processing

Personalized and Emotional Conversation (**PEC**) is defined as follows: Given the personalized information ($P_{R1}$ and $P_{R2}$) of two speakers, their conversation context $C$, the emotion $E_K$ and DA $D_K$ of the response to be generated, and the personalized information $P_{K}$ of the responder, the goal is to generate an anthropomorphic response $Y$.
\begin{equation}
Y = argmax_{Y'}P(Y'|C, E_K, D_K, P_K) \label{task_definition}
\end{equation}

Particularly, context $C=\{(U_1,E_1,D_1,P_1),\cdots,(U_{K-1},E_{K-1},D_{K-1},P_{K-1})\}$ contains multi-turn conversation content (i.e., utterance $U_i$), emotion $E_i$ of the associated utterance, DA $D_i$ of the associated utterance, and personalized information $P_i$ of the associated speaker.
Task: Machine Reading Comprehension
Area: natural-language-processing

**Machine Reading Comprehension** is one of the key problems in Natural Language Understanding, where the task is to read and comprehend a given text passage, and then answer questions based on it.

<span class="description-source">Source: [Making Neural Machine Reading Comprehension Faster ](https://arxiv.org/abs/1904.00796)</span>
Task: Learning with noisy labels
Area: natural-language-processing

Learning with noisy labels means When we say "noisy labels," we mean that an adversary has intentionally messed up the labels, which would have come from a "clean" distribution otherwise. This setting can also be used to cast learning from only positive and unlabeled data.
Task: Commonsense Reasoning for RL
Area: natural-language-processing

Commonsense reasoning for Reinforcement Learning agents
Task: Chinese Word Segmentation
Area: natural-language-processing

Chinese word segmentation is the task of splitting Chinese text (i.e. a sequence of Chinese characters) into words (Source: www.nlpprogress.com).
Task: Paraphrase Generation
Area: natural-language-processing

Paraphrase Generation involves transforming a natural language sentence to a new sentence, that has the same semantic meaning but a different syntactic or lexical surface form.
Task: Sentence Summarization
Area: natural-language-processing

Generating a summary of a given sentence.
Task: Intent Classification
Area: natural-language-processing

**Intent Classification** is the task of correctly labeling a natural language utterance from a predetermined set of intents


<span class="description-source">Source: [Multi-Layer Ensembling Techniques for Multilingual Intent Classification ](https://arxiv.org/abs/1806.07914)</span>
Task: Slot Filling
Area: natural-language-processing

The goal of **Slot Filling** is to identify from a running dialog different slots, which correspond to different parameters of the user’s query. For instance, when a user queries for nearby restaurants, key slots for location and preferred food are required for a dialog system to retrieve the appropriate information. Thus, the main challenge in the slot-filling task is to extract the target entity.


<span class="description-source">Source: [Real-time On-Demand Crowd-powered Entity Extraction ](https://arxiv.org/abs/1704.03627)</span>

Image credit: [Robust Retrieval Augmented Generation for Zero-shot Slot Filling](https://arxiv.org/pdf/2108.13934.pdf)
Task: Paper generation
Area: natural-language-processing

Generating scientific paper texts, such as abstracts.
Task: Extractive Text Summarization
Area: natural-language-processing

Given a document, selecting a subset of the words or sentences which best represents a summary of the document.
Task: Event-Driven Trading
Area: natural-language-processing

Making stock trading decisions based on events.
Task: Suggestion mining
Area: natural-language-processing

Suggestion mining can be defined as the extraction of suggestions from unstructured text,
Task: Recognizing Emotion Cause in Conversations
Area: natural-language-processing

Given an utterance U, labeled with emotion E, the task is to extract the causal spans S from the conversational history H (including utterance U) that sufficiently represent the causes of emotion E.
Task: Text Simplification
Area: natural-language-processing

**Text Simplification** is the task of reducing the complexity of the vocabulary and sentence structure of text while retaining its original meaning, with the goal of improving readability and understanding. Simplification has a variety of important societal applications, for example increasing accessibility for those with cognitive disabilities such as aphasia, dyslexia, and autism, or for non-native speakers and children with reading difficulties.


<span class="description-source">Source: [Multilingual Unsupervised Sentence Simplification](https://arxiv.org/abs/2005.00352)</span>
Task: Morphological Tagging
Area: natural-language-processing

Morphological tagging is the task of assigning labels to a sequence of tokens that describe them morphologically. As compared to Part-of-speech tagging, morphological tagging also considers morphological features, such as case, gender or the tense of verbs.
Task: Claim-Evidence Pair Extraction (CEPE)
Area: natural-language-processing

Since evidence is clearly supporting the corresponding claims in an article, claims and evidence are mutually reinforcing each other in the context. Therefore, we hypothesize the claim extraction task and the evidence extraction task may benefit each other. By combining these two subtasks, we define the second integrated task as: given a specific topic and relevant articles, extract the claim-evidence pairs (CEPs) from the articles.
Task: Taxonomy Expansion
Area: natural-language-processing

Expand a seed taxonomy with new unseen node
Task: Multi-modal Dialogue Generation
Area: natural-language-processing

Image credit: [OpenViDial](https://github.com/ShannonAI/OpenViDial)
Task: Answer Selection
Area: natural-language-processing

**Answer Selection** is the task of identifying the correct answer to a question from a pool of candidate answers. This task can be formulated as a classification or a ranking problem.


<span class="description-source">Source: [Learning Analogy-Preserving Sentence Embeddings for Answer Selection ](https://arxiv.org/abs/1910.05315)</span>
Task: Automatic Writing
Area: natural-language-processing

Generating text based on internal machine representations.
Task: Catalog Extraction
Area: natural-language-processing

Catalog Extraction aims to re-construct catalog-based document trees.
Task: Dialogue Management
Area: natural-language-processing

<span style="color:grey; opacity: 0.6">( Image credit: [Bocklisch et al.](https://arxiv.org/pdf/1712.05181v2.pdf) )</span>
Task: Multimodal Sentiment Analysis
Area: natural-language-processing

Multimodal sentiment analysis is the task of performing sentiment analysis with multiple data sources - e.g. a camera feed of someone's face and their recorded speech.

<span style="color:grey; opacity: 0.6">( Image credit: [ICON: Interactive Conversational Memory Network
for Multimodal Emotion Detection](https://www.aclweb.org/anthology/D18-1280.pdf) )</span>
Task: Task-Oriented Dialogue Systems
Area: natural-language-processing

Achieving a pre-defined task through a dialog.
Task: ARQMath2
Area: natural-language-processing

Answer Retrieval for Questions about Math v2 (2021)
Task: Sonnet Generation
Area: natural-language-processing

Generating a poetry in the form of a sonnet.
Task: Document Summarization
Area: natural-language-processing

Automatic **Document Summarization** is the task of rewriting a document into its shorter form while still retaining its important content. The most popular two paradigms are extractive approaches and abstractive approaches. Extractive approaches generate summaries by extracting parts of the original document (usually sentences), while abstractive methods may generate new words or phrases which are not in the original document.


<span class="description-source">Source: [HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization ](https://arxiv.org/abs/1905.06566)</span>
Task: Nested Mention Recognition
Area: natural-language-processing

Nested mention recognition is the task of correctly modeling the nested structure of mentions.
Task: Clinical Assertion Status Detection
Area: natural-language-processing

Classifying the assertions made on given medical concepts as being present, absent, or possible in the patient, conditionally present in the patient under certain circumstances, hypothetically present in the patient at some future point, and mentioned in the patient report but associated with someoneelse. (e.g. clinical finding pertains to the patient by assigning a label such as present (”patient is diabetic”), absent (”patient denies nausea”), conditional (”dyspnea while climbing stairs”), or associated with someone else (”family history of depression”))

( [Source](https://arxiv.org/pdf/2012.04005v1.pdf) )
Task: Rules-of-thumb Generation
Area: natural-language-processing

Generate relevant rules-of-thumb given text input.
Task: Cross-Lingual Bitext Mining
Area: natural-language-processing

Cross-lingual bitext mining is the task of mining sentence pairs that are translations of each other from large text corpora.
Task: Aspect Sentiment Triplet Extraction
Area: natural-language-processing

Aspect Sentiment Triplet Extraction (ASTE)
is the task of extracting the triplets of target
entities, their associated sentiment, and opinion spans explaining the reason for the sentiment.
Task: Unsupervised Sentence Compression
Area: natural-language-processing

Producing a shorter sentence by removing redundant information, preserving the grammatically and the important content of the original sentence without supervision. (Source: nlpprogress.com)