J0nasW/paperswithcode huggingface dataset:
Task: Dependency Parsing
Area: natural-language-processing

Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical
structure and defines the relationships between "head" words and words, which modify those heads.

Example:

```
     root
      |
      | +-------dobj---------+
      | |                    |
nsubj | |   +------det-----+ | +-----nmod------+
+--+  | |   |              | | |               |
|  |  | |   |      +-nmod-+| | |      +-case-+ |
+  |  + |   +      +      || + |      +      | |
I  prefer  the  morning   flight  through  Denver
```

Relations among the words are illustrated above the sentence with directed, labeled
arcs from heads to dependents (+ indicates the dependent).
Task: Unsupervised Machine Translation
Area: natural-language-processing

Unsupervised machine translation is the task of doing machine translation without any translation resources at training time.

<span style="color:grey; opacity: 0.6">( Image credit: [Phrase-Based & Neural Unsupervised Machine Translation](https://arxiv.org/pdf/1804.07755v2.pdf) )</span>
Task: Multi-Label Text Classification
Area: natural-language-processing

According to Wikipedia "In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to."
Task: Table annotation
Area: natural-language-processing

**Table annotation** is the task of annotating a table with terms/concepts from knowledge graph or database schema. Table annotation is typically broken down into the following five subtasks: 

1. Cell Entity Annotation ([CEA](https://paperswithcode.com/task/cell-entity-annotation))
2. Column Type Annotation ([CTA](https://paperswithcode.com/task/column-type-annotation))
3. Column Property Annotation ([CPA](https://paperswithcode.com/task/columns-property-annotation))
4. [Table Type Detection](https://paperswithcode.com/task/table-type-detection)
5. [Row Annotation](https://paperswithcode.com/task/row-annotation)

The [SemTab](http://www.cs.ox.ac.uk/isg/challenges/sem-tab/) challenge is closely related to the Table Annotation problem. It is a yearly challenge which focuses on the first three tasks of table annotation and its purpose is to benchmark different table annotation systems.
Task: Temporal Relation Classification
Area: natural-language-processing

Temporal Relation Classification is the task that is concerned with classifying the temporal relation between a pair of temporal entities (traditional events and temporal expressions). Initial approaches aimed to classify the temporal relation in thirteen relation types that were depicted by James Allen in his seminal work "Maintaining Knowledge about Temporal Intervals". However, due to the ambiguity in the annotation, recent corpora have been limiting the type of relations to a subset of those relations.

Notice that although Temporal Relation Classification can be thought of as a subtask of Temporal Relation Extraction, the two tasks can be morphed if one adds a label that indicates the absence of a temporal relation between the entities (e.g. "no_relation" or "vague") to Temporal Relation Classification.
Task: Semantic entity labeling
Area: natural-language-processing

- One of Form Understanding task (Word grouping, Semantic entity labeling, Entity linking)
- Classifying entities into one of four pre-defined categories: question, answer, header and, other.

cited from

G. Jaume, H. K. Ekenel, J. Thiran "FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents," 2019
Task: Active Learning
Area: natural-language-processing

**Active Learning** is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model


<span class="description-source">Source: [Polystore++: Accelerated Polystore System for Heterogeneous Workloads ](https://arxiv.org/abs/1905.10336)</span>
Task: AMR Parsing
Area: natural-language-processing

Each AMR is a single rooted, directed graph. AMRs include PropBank semantic roles, within-sentence coreference, named entities and types, modality, negation, questions, quantities, and so on. [See](https://amr.isi.edu/index.html).
Task: Community Question Answering
Area: natural-language-processing

Community question answering is the task of answering questions on a Q&A forum or board, such as Stack Overflow or Quora.
Task: Twitter Event Detection
Area: natural-language-processing

Detection of worldwide events from categories like Sports, Politics, Entertainment, Science & Technology, etc. by analyzing Twitter Tweets.
Task: Turning Point Identification
Area: natural-language-processing

Identification of key events in a narrative (such as movie or TV episode). The task is supported by screenwriting theory, according to which there are 5 different types of key events in a movie. These key events (e.g., change of plans, major setback, climax) are crucial narrative moments: they define the plot structure and determine its progression and thematic units (e.g., setup, complications, aftermath).
Task: Aspect Extraction
Area: natural-language-processing

Aspect extraction is the task of identifying and extracting terms relevant for opinion mining and sentiment analysis, for example terms for product attributes or features.
Task: Hope Speech Detection for English
Area: natural-language-processing

Detecting Hope Speech in the English Language
Task: Drug–drug Interaction Extraction
Area: natural-language-processing

Automatic extraction of Drug-drug interaction (DDI) information from the biomedical literature.

( Image credit: [Using Drug Descriptions and Molecular Structures for Drug-Drug Interaction Extraction from Literature](https://watermark.silverchair.com/btaa907.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAuAwggLcBgkqhkiG9w0BBwagggLNMIICyQIBADCCAsIGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMb9BaAetiYVbvf0_9AgEQgIICk0-IUccCHnqrDDtbnyBSTvPnrWXc4F2vfEMKkxzGRA3-WbynJbw0HptyHyjawXr3c4TeC9ZhIta1szzhc5t1JOhdh8rOo5CTtGk_JPfq14TkMkIISDCvdsVL76fvCn-3KhtAJhHPcyYTDqMaGSb3ltXJLfR6PXoCXnINprcZ3pO6ktuNLA8KF0_CHTITnGNcX1G1n6ZERyGTPwGjodH9Qq9UcYzJCx4N1KKgxOMAj5oxIoLPQi97oNJ3eCoYBoKDuSW-Zza_ULcBDXkkTvt3A460O32vfRAqnnPYlVSuvEiASc3lX8O6Qb28GXc99EIzwDQJEO4znl7haAGOxubuUm5Of9p22xRfc7KSuCh41cJxW_31bsnTuf8Sb2z6O6bFo3aNlxF3SrLfNTHfJH6Tst0WvaaRJ1gQ3JxcFwu--hsZMF9bW3_yzFwP1ZnVIPVtE0bqm3QZt-_nHaE4o9KgCCNY4t70h7U7yD9ZrZMvFnNieSvgL35t--l5PerE9uZgP6v9LUKUyAI1TDHHMameO5794Z7WII8v_MKG0jmUIbr564ENnyKJYunNowu3EbvUMxBv3DSUkepU1kP66tJjSflal3LlRd8LUbj4m2Tin3LteS-09Gje4pyMaeO5RywX_tSPSFGK8QGaZqpgbIU2y23YnCMAnIDOSS3_L3LQRGu50YK5OwEvMY7azpEMbR3kaaHWu_cmEN2Vm61UdG6uLql5pMc9zKfGAQ3E1VXGuhdEalRtuLbtBZ9UFj-vYePfrRGWRXjkg-11SGgKqRaJcLK32yuvhG11KqacCoY169A4G29_GfRC3rbDsnqyMRZ9ESe3FB1NnWwZ4HPNA7ju3yJ7cfZSfYgdygT6oTpBQjaweH5U) )
Task: Personality Recognition in Conversation
Area: natural-language-processing

Given a speaker's conversation with others, it is required to recognize the speaker's personality traits through the conversation record, which includes two scenarios, (1) $1-1$ conversations: the robot recognizes the personality traits of the speaker through the conversation between them (e.g., psychological counseling), (2) $1-N$ conversations : the robot listens to the speaker's conversations with other $N$ people and then recognizes the speaker's personality traits (e.g., group chatbot, home service robot). Since $1-N$ includes the case of $1-1$, we only discusses PRC in $1-N$ conversations. The task of PRC in $1-N$ conversations can be formulated as:

$Per_i = argmax_{Per'_i}P(Per'_i | C_{i,j}, \cdots, C_{i,N})$

where $Per_i=[Neu, Ext, Ope, Agr, Con]$ is a 5-dimensional vector representing Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. $C_{i,j}$ is the conversations between $Speaker_i$ and $Speaker_j$ ($1 \leq j \leq N$).
Task: Word Attribute Transfer
Area: natural-language-processing

Changing a word's attribute, such as its gender.
Task: Hungarian Text Diacritization
Area: natural-language-processing

Addition of diacritics for undiacritized Hungarian Wikipedia texts.
Task: Table Retrieval
Area: natural-language-processing

When given a query, the goal of this task is to retrieve a relevant table from a (potentially large) collection of tables. The query could be a single sentence (such as a question), or it could also be a conversation. As for the retrieval, the tables could be in the raw form (i.e. the values of each cells), the metadata (such as the title, description), or summary statistics.
Task: Multimodal Text Prediction
Area: natural-language-processing

**Multimodal text prediction** is a type of natural language processing that involves predicting the next word or sequence of words in a sentence, given multiple modalities or types of input. In traditional text prediction, the prediction is based solely on the context of the sentence, such as the words that precede the target word. In multimodal text prediction, additional modalities, such as images, audio, or user behavior, are also used to inform the prediction.

For example, in a multimodal text prediction system for captioning images, the system may use both the content of the image and the words that have been typed so far to generate the next word in the caption. The image may provide additional context or information about the content of the caption, while the typed words may provide information about the style or tone of the caption.

Multimodal text prediction can be achieved using a variety of techniques, including deep learning models and statistical models. These models can be trained on large datasets of text and multimodal inputs to learn the relationships between the different types of data and improve the accuracy of the predictions.

Multimodal text prediction has many applications, including chatbots, virtual assistants, and predictive text input for mobile devices. By incorporating additional modalities into the prediction process, multimodal text prediction systems can provide more accurate and useful predictions, improving the overall user experience.
Task: Semantic Role Labeling
Area: natural-language-processing

Semantic role labeling aims to model the predicate-argument structure of a sentence
and is often described as answering "Who did what to whom". BIO notation is typically
used for semantic role labeling.

Example:

| Housing | starts | are | expected | to | quicken | a | bit | from | August’s | pace | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| B-ARG1 | I-ARG1 | O |  O  |  O  |   V  | B-ARG2 | I-ARG2 | B-ARG3 | I-ARG3 | I-ARG3 |
Task: Prompt Engineering
Area: natural-language-processing

**Prompt engineering** is the process of designing and refining the prompts used to generate text from language models, such as GPT-3 or similar models. The goal of prompt engineering is to improve the quality and relevance of the generated text by carefully crafting the prompts to elicit the desired responses from the model.

Prompt engineering involves several steps, including selecting the appropriate model architecture and parameters, designing the prompt format and structure, selecting the appropriate task and training data, and fine-tuning the model using the selected prompt and data.

Prompt engineering is a crucial step in the development of language models, as it can greatly influence the quality and effectiveness of the model's responses. By carefully designing and refining the prompts used to generate text, researchers and developers can improve the accuracy and relevance of the model's output, making it more useful for a wide range of applications, including chatbots, language translation, content creation, and more.
Task: Graph-to-Sequence
Area: natural-language-processing

Mapping an input graph to a sequence of vectors.
Task: Stance Detection
Area: natural-language-processing

Stance detection is the extraction of a subject's reaction to a claim made by a primary actor. It is a core part of a set of approaches to fake news assessment.

Example:

* Source: "Apples are the most delicious fruit in existence"
* Reply: "Obviously not, because that is a reuben from Katz's"
* Stance: deny
Task: Empathetic Response Generation
Area: natural-language-processing

Generate empathetic responses in dialogues
Task: Connective Detection
Area: natural-language-processing

Misha packed carefully so that everything would fit in her bag.
Task: Molecular description generation
Area: natural-language-processing

Molecular description generation entails the creation of a detailed textual depiction illuminating the structure, properties, biological activity, and applications of a molecule based on its molecular descriptors. It furnishes chemists and biologists with a swift conduit to essential molecular information, thus efficiently guiding their research and experiments.
Task: Document Ranking
Area: natural-language-processing

Sort documents according to some criterion so that the "best" results appear early in the result list displayed to the user (Source: Wikipedia).
Task: Unsupervised Dependency Parsing
Area: natural-language-processing

Unsupervised dependency parsing is the task of inferring the dependency parse of sentences without any labeled training data.

Description from [NLP Progress](http://nlpprogress.com/english/dependency_parsing.html)
Task: Part-Of-Speech Tagging
Area: natural-language-processing

Part-of-speech tagging (POS tagging) is the task of tagging a word in a text with its part of speech.
A part of speech is a category of words with similar grammatical properties. Common English
parts of speech are noun, verb, adjective, adverb, pronoun, preposition, conjunction, etc.

Example: 

| Vinken | , | 61 | years | old |
| --- | ---| --- | --- | --- |
| NNP | , | CD | NNS | JJ |
Task: Readability optimization
Area: natural-language-processing

It consists of improving the readability of a text automatically and without significantly altering the form or meaning.
Task: Misogynistic Aggression Identification
Area: natural-language-processing

Develop a binary classifier for classifying the text as ‘gendered’ or ‘non-gendered’. For this, the TRAC-2 dataset of 5,000 annotated data from social media each in Bangla (in both Roman and Bangla script), Hindi (in both Roman and Devanagari script) and English for training and validation is to be used.
Task: Few-shot NER
Area: natural-language-processing

Few-Shot Named Entity Recognition (NER) is the task of recognising a 'named entity' like a person, organization, time and so on in a piece of text e.g. "Alan Mathison [person] visited the Turing Institute [organization] in June [time].
Task: Irony Identification
Area: natural-language-processing

This task asks a model to identify whether a given sentence(s) is/are ironic or not.

Source: [BIG-bench](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/irony_identification)
Task: Abstractive Text Summarization
Area: natural-language-processing

**Abstractive Text Summarization** is the task of generating a short and concise summary that captures the salient ideas of the source text. The generated summaries potentially contain new phrases and sentences that may not appear in the source text.


<span class="description-source">Source: [Generative Adversarial Network for Abstractive Text Summarization ](https://arxiv.org/abs/1711.09357)</span>

Image credit: [Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond](https://arxiv.org/pdf/1602.06023v5.pdf)
Task: Relation Extraction
Area: natural-language-processing

**Relation Extraction** is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.


<span class="description-source">Source: [Deep Residual Learning for Weakly-Supervised Relation Extraction ](https://arxiv.org/abs/1707.08866)</span>
Task: Timex normalization
Area: natural-language-processing

Temporal expression normalisation is the grounding of a lexicalisation of a time to a calendar date or other formal temporal representation.

Example:

10/18/2000 21:01:00.65
Dozens of Palestinians were wounded in scattered clashes in the West Bank and Gaza Strip, Wednesday, despite the Sharm el-Sheikh truce accord.

Chuck Rich reports on entertainment every Saturday

Description from [NLP Progress](http://nlpprogress.com/english/temporal_processing.html)
Task: Semantic Composition
Area: natural-language-processing

Understanding the meaning of text by composing the meanings of the individual words in the text (Source: https://arxiv.org/pdf/1405.7908.pdf)
Task: Emotion Recognition in Conversation
Area: natural-language-processing

Given the transcript of a conversation along with speaker information of each constituent utterance, the ERC task aims to identify the emotion of each utterance from several pre-defined emotions. Formally, given the input sequence of N number of utterances [(u1, p1), (u2, p2), . . . , (uN , pN )], where each utterance ui = [ui,1, ui,2, . . . , ui,T ] consists of T words ui,j and spoken by party pi, the task is to predict the emotion label ei of each utterance ui.
.
Task: UCCA Parsing
Area: natural-language-processing

UCCA (Abend and Rappoport, 2013) is a semantic representation whose main design principles are ease of annotation, cross-linguistic applicability, and a modular architecture. UCCA represents the semantics of linguistic utterances as directed acyclic graphs (DAGs), where terminal (childless) nodes correspond to the text tokens, and non-terminal nodes to semantic units that participate in some super-ordinate relation. Edges are labeled, indicating the role of a child in the relation the parent represents. UCCA’s foundational layer mostly covers predicate-argument structure, semantic heads and inter-Scene relations. UCCA distinguishes primary edges, corresponding to explicit relations, from remote edges that allow for a unit to participate in several super-ordinate relations. Primary edges form a tree in each layer, whereas remote edges enable reentrancy, forming a DAG.

Description from [NLP Progress](http://nlpprogress.com/english/semantic_parsing.html)
Task: Bias Detection
Area: natural-language-processing

Bias detection is the task of detecting and measuring racism, sexism and otherwise discriminatory behavior in a model (Source: https://stereoset.mit.edu/)
Task: Native Language Identification
Area: natural-language-processing

Native Language Identification (NLI) is the task of determining an author's native language (L1) based only on their writings in a second language (L2).
Task: Phrase Vector Embedding
Area: natural-language-processing

Just like the generation of word (1-gram) vector embedding, this task is for phrase (n-gram) vector embedding.
Task: Relation Mention Extraction
Area: natural-language-processing

Extracting phrases representative for a specific relation.
Task: Text Summarization
Area: natural-language-processing

**Text Summarization** is a natural language processing (NLP) task that involves condensing a lengthy text document into a shorter, more compact version while still retaining the most important information and meaning. The goal is to produce a summary that accurately represents the content of the original text in a concise form. 

There are different approaches to text summarization, including extractive methods that identify and extract important sentences or phrases from the text, and abstractive methods that generate new text based on the content of the original text.
Task: Keyword Extraction
Area: natural-language-processing

Keyword extraction is tasked with the automatic identification of terms that best describe the subject of a document (Source: Wikipedia).
Task: Aspect-Based Sentiment Analysis (ABSA)
Area: natural-language-processing

**Aspect-Based Sentiment Analysis (ABSA)** is a Natural Language Processing task that aims to identify and extract the sentiment of specific aspects or components of a product or service. ABSA typically involves a multi-step process that begins with identifying the aspects or features of the product or service that are being discussed in the text. This is followed by sentiment analysis, where the sentiment polarity (positive, negative, or neutral) is assigned to each aspect based on the context of the sentence or document. Finally, the results are aggregated to provide an overall sentiment for each aspect.

And recent works propose more challenging ABSA tasks to predict sentiment triplets or quadruplets (Chen et al., 2022), the most influential of which are ASTE (Peng et al., 2020; Zhai et al., 2022), TASD (Wan et al., 2020), ASQP (Zhang et al., 2021a) and ACOS with an emphasis on the implicit aspects or opinions (Cai et al., 2020a).

<span style="color:grey; opacity: 0.6">( Source: [MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction](https://arxiv.org/abs/2305.12627) )</span>
Task: Query-Based Extractive Summarization
Area: natural-language-processing

Extracting summarized information that answers a given query based on a reference text.
Task: Named Entity Recognition (NER)
Area: natural-language-processing

**Named Entity Recognition (NER)** is a task of Natural Language Processing (NLP) that involves identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, and others. The goal of NER is to extract structured information from unstructured text data and represent it in a machine-readable format. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.

Example:

| Mark | Watney | visited | Mars |
| --- | ---| --- | --- |
| B-PER | I-PER | O | B-LOC |

<span style="color:grey; opacity: 0.6">( Image credit: [Zalando](https://research.zalando.com/welcome/mission/research-projects/flair-nlp/) )</span>
Task: Arabic Sentiment Analysis
Area: natural-language-processing

Arabic sentiment analysis is the process of computationally identifying and categorizing opinions expressed in a piece of arabic text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral (Source: Oxford Languages)
Task: Authorship Verification
Area: natural-language-processing

Authorship verification (**AV**) is a research subject in the field of digital text forensics that concerns itself with the question, whether two documents have been written by the same person. 

Definition taken from the paper **Assessing the Applicability of Authorship Verification Methods**,
available at: <https://arxiv.org/abs/1906.10551>
Task: Lexical Normalization
Area: natural-language-processing

Lexical normalization is the task of translating/transforming a non standard text to a standard register.

Example:

```
new pix comming tomoroe
new pictures coming tomorrow
```

Datasets usually consists of tweets, since these naturally contain a fair amount of 
these phenomena.

For lexical normalization, only replacements on the word-level are annotated.
Some corpora include annotation for 1-N and N-1 replacements. However, word
insertion/deletion and reordering is not part of the task.