J0nasW/paperswithcode huggingface dataset:
Task: Knowledge Base Population
Area: natural-language-processing

Knowledge base population is the task of filling the incomplete elements of a given knowledge base by automatically processing a large corpus of text.
Task: Overlapping Mention Recognition
Area: natural-language-processing

Overlapping mention recognition is the task of correctly identifying all mentions of an entity in the presence of overlapping entity mentions.
Task: Cross-Document Language Modeling
Area: natural-language-processing

Involves pretraining language models to support multi-document NLP tasks.

Source: [Cross-Document Language Modeling](https://arxiv.org/pdf/2101.00406v1.pdf)

Image Credit: [Cross-Document Language Modeling](https://arxiv.org/pdf/2101.00406v1.pdf)
Task: Crowdsourced Text Aggregation
Area: natural-language-processing

One of the most important parts of processing responses from crowd workers is **aggregation**: given several conflicting opinions, a method should extract the truth. This problem is also known as *truth-inference* in crowdsourcing. Text aggregation problem is dedicated to extracting the correct information from crowd workers' responses for a crowdsourcing task where the output is a *text*: audio transcription, translation, character recognition, etc.
Task: Open-Ended Question Answering
Area: natural-language-processing

Open-ended questions are defined as those that simply pose the question, without imposing any constraints on the format of the response. This distinguishes them from questions with a predetermined answer format.
Task: Transliteration
Area: natural-language-processing

**Transliteration** is a mechanism for converting a word in a source (foreign) language to a target language, and often adopts approaches from machine translation. In machine translation, the objective is to preserve the semantic meaning of the utterance as much as possible while following the syntactic structure in the target language. In Transliteration, the objective is to preserve the original pronunciation of the source word as much as possible while following the phonological structures of the target language.

For example, the city’s name “Manchester” has become well known by people of languages other than English. These new words are often named entities that are important in cross-lingual information retrieval, information extraction, machine translation, and often present out-of-vocabulary challenges to spoken language technologies such as automatic speech recognition, spoken keyword search, and text-to-speech.


<span class="description-source">Source: [Phonology-Augmented Statistical Framework for Machine Transliteration using Limited Linguistic Resources ](https://arxiv.org/abs/1810.03184)</span>
Task: Joint NER and Classification
Area: natural-language-processing

Joint named entity recognition and classification refers to the combined task of identifying named entitites in a given text and text classification.
Task: Multi-modal Named Entity Recognition
Area: natural-language-processing

Multi-modal named entity recognition aims at improving
the accuracy of NER models through utilizing image information.
Task: Document Dating
Area: natural-language-processing

Document Dating is the problem of automatically predicting the date of a document based on its content. Date of a document, also referred to as the Document Creation Time (DCT), is at the core of many important tasks, such as, information retrieval, temporal reasoning, text summarization, event detection, and analysis of historical text, among others.

For example, in the following document, the correct creation year is 1999. This can be inferred by the presence of terms 1995 and Four years after.

Swiss adopted that form of taxation in 1995. The concession was approved by the govt last September. Four years after, the IOC….

Description from [NLP Progress](http://nlpprogress.com/english/temporal_processing.html)
Task: Key Information Extraction
Area: natural-language-processing

Key Information Extraction (KIE) is aimed at extracting structured information (e.g. key-value pairs) from form-style documents (e.g. invoices), which makes an important step towards intelligent document understanding.
Task: Empirical Judgments
Area: natural-language-processing

Drawing inspiration from Immanuel Kant, this task measures a model’s ability to distinguish between two kinds of empirical judgments: judgments that assert a correlative relation between empirical events, and judgments that assert a causal relation.

Source: [BIG-bench](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/empirical_judgments)
Task: Description-guided molecule generation
Area: natural-language-processing

The significance of description-based molecule generation lies in its potential to streamline the process of molecular design by enabling the production of molecules that directly meet the criteria outlined in a given description. This facilitates a more targeted approach in the creation and optimization of novel molecules, with applications in diverse fields such as drug discovery and materials science.
Task: Chinese Spell Checking
Area: natural-language-processing

Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for user-generated text in Chinese language.
Task: Low Resource Named Entity Recognition
Area: natural-language-processing

Low resource named entity recognition is the task of using data and models available for one language for which ample such resources are available (e.g., English) to solve named entity recognition tasks in another, commonly more low-resource, language.
Task: Slovak Text Diacritization
Area: natural-language-processing

Addition of diacritics for undiacritized Slovak Wikipedia texts.
Task: Relation Classification
Area: natural-language-processing

**Relation Classification** is the task of identifying the semantic relation holding between two nominal entities in text.


<span class="description-source">Source: [Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text ](https://arxiv.org/abs/1803.05662)</span>
Task: Conversation Disentanglement
Area: natural-language-processing

Automatic disentanglement could be used to provide more interpretable results when searching over chat logs, and to help users understand what is happening when they join a channel. 

Source: [Kummerfeld et al.](https://arxiv.org/pdf/1810.11118v2.pdf)
Task: Natural Language Transduction
Area: natural-language-processing

Converting one sequence into another
Task: Cause-Effect Relation Classification
Area: natural-language-processing

Classifying pairs of entities (e.g., events) into causal or non-causal or predicting Cause and Effect in a causal relation
Task: Open Intent Detection
Area: natural-language-processing

Open intent detection aims to identify n-class known intents, and detect one-class open intent.
Task: Keyphrase Generation
Area: natural-language-processing

Keyphrase Generation aims at generating keyphrases (or phrases) that best summarize a given text article or document.
Task: Distractor Generation
Area: natural-language-processing

Given a passage, a question, and an answer phrase, the goal of distractor generation (DG) is to generate context-related wrong options (i.e., distractor) for multiple-choice questions (MCQ).
Task: Multilingual Machine Comprehension in English Hindi
Area: natural-language-processing

Multilingual Machine Comprehension (MMC) is a Question-Answering (QA) sub-task that involves quoting the answer for a question from a given snippet, where the question and the snippet can be in different languages. Results on an extended version of the recently released XQuAD dataset, which we propose to use as the evaluation benchmark for future research.
Task: Aspect-oriented  Opinion Extraction
Area: natural-language-processing

Extracting the paired opinion terms for every given aspect term in a sentence.
Task: Sentence Ordering
Area: natural-language-processing

Sentence ordering task deals with finding the correct order of sentences given a randomly ordered paragraph.
Task: Question Quality Assessment
Area: natural-language-processing

This task expects to build subjective question-answering algorithms to check whether a question is of high quality or needs to be edited/flagged.
Task: Open Intent Discovery
Area: natural-language-processing

Open intent discovery aims to leverage limited prior knowledge of known intents to find fine-grained known and open intent-wise clusters.
Task: Joint Entity and Relation Extraction
Area: natural-language-processing

Scores reported from systems which jointly extract entities and relations.
Task: Semantic Textual Similarity
Area: natural-language-processing

Semantic textual similarity deals with determining how similar two pieces of texts are.
This can take the form of assigning a score from 1 to 5. Related tasks are paraphrase or duplicate identification.

Image source: [Learning Semantic Textual Similarity from Conversations](https://arxiv.org/pdf/1804.07754.pdf)
Task: Hurtful Sentence Completion
Area: natural-language-processing

Measure hurtful sentence completions in language models (HONEST)
Task: Multiview Contextual Commonsense Inference
Area: natural-language-processing

Depending on the situation, multiple different reasonings are possible each leading to various unique inferences. For a given context, identifying all valid inferences require commonsense reasoning and as such, this task is called Multiview Contextual Commonsense Inference.
Task: Aggression Identification
Area: natural-language-processing

Develop a classifier that could make a 3-way classification in-between ‘Overtly Aggressive’, ‘Covertly Aggressive’ and ‘Non-aggressive’ text data. For this, TRAC-2 dataset of 5,000 aggression-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: Multimodal Machine Translation
Area: natural-language-processing

Multimodal machine translation is the task of doing machine translation with multiple data sources - for example, translating "a bird is flying over water" + an image of a bird over water to German text.

<span style="color:grey; opacity: 0.6">( Image credit: [Findings of the Third Shared Task on Multimodal Machine Translation](https://www.aclweb.org/anthology/W18-6402.pdf) )</span>
Task: Comment Generation
Area: natural-language-processing

Article commenting poses new challenges for machines, as it involves multiple cognitive abilities: understanding the given article, formulating opinions and arguments, and organizing natu ral language for expression.
Task: Emergent communications on relations
Area: natural-language-processing

Emergent communications in the context of relations.
Task: Automated Writing Evaluation
Area: natural-language-processing

Automated writing evaluation refers to the task of analysing and measuring written text based on features, such as syntax, text complexity and vocabulary range.
Task: Multi-Document Summarization
Area: natural-language-processing

**Multi-Document Summarization** is a process of representing a set of documents with a short piece of text by capturing the relevant information and filtering out the redundant information. Two prominent approaches to Multi-Document Summarization are extractive and abstractive summarization. Extractive summarization systems aim to extract salient snippets, sentences or passages from documents, while abstractive summarization systems aim to concisely paraphrase the content of the documents.


<span class="description-source">Source: [Multi-Document Summarization using Distributed Bag-of-Words Model ](https://arxiv.org/abs/1710.02745)</span>
Task: Entity Alignment
Area: natural-language-processing

**Entity Alignment** is the task of finding entities in two knowledge bases that refer to the same real-world object. It plays a vital role in automatically integrating multiple knowledge bases.  
Note: results that have incorporated machine translated entity names (introduced in the RDGCN paper) or pre-alignment name embeddings are considered to have used **extra training labels** (both are marked with "Extra Training Data" in the leaderboard) and are **not adhere to a comparable setting** with others that have followed the original setting of the benchmark.

<span class="description-source">Source: [Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding ](https://arxiv.org/abs/1708.05045)</span>

The task of entity alignment is related to the task of [entity resolution](https://paperswithcode.com/task/entity-resolution) which focuses on matching structured entity descriptions in different contexts.
Task: Visual Commonsense Tests
Area: natural-language-processing

Predict 5 property types (color, shape, material, size, and visual co-occurrence) for over 5000 subjects.
Task: Emotional Dialogue Acts
Area: natural-language-processing

Associating Emotions and Dialogue Acts to find unique relationships between them such as Accept/Agree dialogue acts often occur with the Joy emotion, Apology with Sadness, or Thanking with Joy. 
First introduced in the paper EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators, LREC 2020 (https://aclanthology.org/2020.lrec-1.78/).
Task: Polyphone disambiguation
Area: natural-language-processing

A part of the TTS-front end framework which serves to predict the correct pronunciation for the input polyphone characters.
Task: Passage Re-Ranking
Area: natural-language-processing

Passage re-ranking is the task of scoring and re-ranking a collection of retrieved documents based on an input query.
Task: Entity Typing
Area: natural-language-processing

**Entity Typing** is an important task in text analysis. Assigning types (e.g., person, location, organization) to mentions of entities in documents enables effective structured analysis of unstructured text corpora. The extracted type information can be used in a wide range of ways (e.g., serving as primitives for information extraction and knowledge base (KB) completion, and assisting question answering). Traditional Entity Typing systems focus on a small set of coarse types (typically fewer than 10). Recent studies work on a much larger set of fine-grained types which form a tree-structured hierarchy (e.g., actor as a subtype of artist, and artist is a subtype of person).


<span class="description-source">Source: [Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding ](https://arxiv.org/abs/1602.05307)</span>

<span class="description-source">Image Credit: [Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding ](https://arxiv.org/abs/1602.05307)</span>
Task: Lexical Complexity Prediction
Area: natural-language-processing

Predicting the complexity of a word/multi-word expression in a sentence.
Task: Passage Retrieval
Area: natural-language-processing

**Passage retrieval** is a specialized type of IR application that retrieves relevant passages (or pieces of text) rather than an entire ranked set of documents.
Task: Data-to-Text Generation
Area: natural-language-processing

A classic problem in natural-language generation (NLG) involves taking structured data, such as a table, as input, and producing text that adequately and fluently describes this data as output. Unlike machine translation, which aims for complete transduction of the sentence to be translated, this form of NLG is usually taken to require addressing (at least) two separate challenges: what to say, the selection of an appropriate subset of the input data to discuss, and how to say it, the surface realization of a generation. 

<span style="color:grey; opacity: 0.6">( Image credit: [Data-to-Text Generation with Content Selection and Planning](https://arxiv.org/pdf/1809.00582v2.pdf) )</span>
Task: Abstract Argumentation
Area: natural-language-processing

Identifying argumentative statements from natural language dialogs.
Task: Satire Detection
Area: natural-language-processing

Satire detection consists in detecting when a text is written in a satirical tone and its content shouldn't be interpreted literally.
Task: De-identification
Area: natural-language-processing

De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data.
Task: Aspect-Category-Opinion-Sentiment Quadruple Extraction
Area: natural-language-processing

Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction is the task with the goal to extract all aspect-category-opinion-sentiment quadruples in a review sentence. ( and provide full support for aspect-level sentiment analysis with implicit aspects and opinions if possible )
Task: Chinese Zero Pronoun Resolution
Area: natural-language-processing

Chinese zero pronoun resolution refers to the task of resolving a so-called zero segment of a Chinese text that is not written but supplies information for interpreting the text.
Task: Extreme Summarization
Area: natural-language-processing

Image credit: [TLDR: Extreme Summarization of Scientific Documents](https://arxiv.org/pdf/2004.15011v3.pdf)
Task: Entity Disambiguation
Area: natural-language-processing

**Entity Disambiguation** is the task of linking mentions of ambiguous entities to their referent entities in a knowledge base such as Wikipedia.


<span class="description-source">Source: [Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation ](https://arxiv.org/abs/1504.07678)</span>
Task: Topic Models
Area: natural-language-processing

A topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for the discovery of hidden semantic structures in a text body.
Task: Abuse Detection
Area: natural-language-processing

Abuse detection is the task of identifying abusive behaviors, such as hate speech, offensive language, sexism and racism, in utterances from social media platforms (Source: https://arxiv.org/abs/1802.00385).
Task: Nested Named Entity Recognition
Area: natural-language-processing

Nested named entity recognition is a subtask of information extraction that seeks to locate and classify nested named entities (i.e., hierarchically structured entities) mentioned in unstructured text (Source: Adapted from Wikipedia).
Task: Parallel Corpus Mining
Area: natural-language-processing

Mining a corpus of bilingual sentence pairs that are translations of each other.
Task: Negation Detection
Area: natural-language-processing

Negation detection is the task of identifying negation cues in text.
Task: Relationship Extraction (Distant Supervised)
Area: natural-language-processing

Relationship extraction is the task of extracting semantic relationships from a text. Extracted relationships usually
occur between two or more entities of a certain type (e.g. Person, Organisation, Location) and fall into a number of
semantic categories (e.g. married to, employed by, lives in).
Task: Sequential Pattern Mining
Area: natural-language-processing

**Sequential Pattern Mining** is the process that discovers relevant patterns between data examples where the values are delivered in a sequence.


<span class="description-source">Source: [Big Data Analytics for Large Scale Wireless Networks: Challenges and Opportunities ](https://arxiv.org/abs/1909.08069)</span>
Task: Arabic Text Diacritization
Area: natural-language-processing

Addition of diacritics for undiacritized arabic texts for words disambiguation.
Task: Story Generation
Area: natural-language-processing

Story generation is the task of automatically generating a coherent narrative, often from a set of premises or a brief summary.
Task: target-oriented opinion words extraction
Area: natural-language-processing

The objective of TOWE is to extract the corresponding opinion words describing or evaluating the target from the review.
Task: Persona Dialogue in Story
Area: natural-language-processing

Building persona dialogue in a story
Task: Rumour Detection
Area: natural-language-processing

Rumor detection is the task of identifying rumors, i.e. statements whose veracity is not quickly or ever confirmed, in utterances on social media platforms.
Task: Paraphrase Identification
Area: natural-language-processing

The goal of **Paraphrase Identification** is to determine whether a pair of sentences have the same meaning.


<span class="description-source">Source: [Adversarial Examples with Difficult Common Words for Paraphrase Identification ](https://arxiv.org/abs/1909.02560)</span>

Image source: [On Paraphrase Identification Corpora ](http://www.lrec-conf.org/proceedings/lrec2014/pdf/1000_Paper.pdf)
Task: Phrase Grounding
Area: natural-language-processing

Given an image and a corresponding caption, the **Phrase Grounding** task aims to ground each entity mentioned by a noun phrase in the caption to a region in the image.


<span class="description-source">Source: [Phrase Grounding by Soft-Label Chain Conditional Random Field ](https://arxiv.org/abs/1909.00301)</span>
Task: Extract Aspect
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: multi-word expression embedding
Area: natural-language-processing

Learn embeddings for multi-word expressions
Task: Opinion Mining
Area: natural-language-processing

Identifying and categorizing opinions expressed in a piece of 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)

Image Source: [Deep learning for sentiment analysis: A survey](https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1253)
Task: Concept-To-Text Generation
Area: natural-language-processing

Generating natural language text from a conceptualized representation, such as an ontology.
Task: Conversational Response Selection
Area: natural-language-processing

Conversational response selection refers to the task of identifying the most relevant response to a given input sentence from a collection of sentences.
Task: Language Acquisition
Area: natural-language-processing

Language acquisition refers to tasks related to the learning of a second language.
Task: Blocking
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)

**Blocking** is a crucial step in any entity resolution pipeline because a pair-wise comparison of all records across two data sources is infeasible. 
Blocking applies a computationally cheap method to generate a smaller set of candidate record pairs reducing the workload of the matcher.
During matching a more expensive pair-wise matcher generates a final set of matching record pairs.


Survey on blocking:

- [Papadakis et al.: Blocking and Filtering Techniques for Entity Resolution: A Survey](https://dl.acm.org/doi/pdf/10.1145/3377455), 2020.