NLP Tasks

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Introduction

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Processing words of any language and driving some meaning from these is as old as the human language. Recently, AI momentum is taking on many of these language-processing tasks. Here is the summary of these NLP tasks, this list is continuously growing. Researchers keep creating a dataset for these tasks in different languages. Other researchers keep devising new ways to solve these tasks with better performance. They come up with a new architecture, a new set of hyperparameters, a new pipeline, etc. In summary, as of today, there are around 55 tasks. Hundreds of datasets and research papers exist around these. You can check on PaperWithCode or Hggingface

Key NLP Tasks

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Some of these tasks are self-explanatory, so I have avoided describing them. Other tasks I tried to explain with examples.

1. Arithmetic Reasoning:
2. Causal Judgment:
   • Determining the causal relationship between two events: In this task, the NLP model is given a pair of events, and the model must determine whether the first event caused the second event. For example, given the events “The car broke down” and “The driver ran out of gas,” the NLP model should be able to determine that the first event caused the second event.
   • Determining the direction of causality: In this task, the NLP model is given a pair of events, and the model must determine whether the first event causes the second event or the second event causes the first event. For example, given the events “The car broke down” and “The driver ran out of gas,” the NLP model should be able to determine that the first event caused the second event, not the other way around.
   • Determining the strength of the causal relationship: In this task, the NLP model is given a pair of events, and the model must determine how strong the causal relationship is between the two events. For example, given the events “The car broke down” and “The driver ran out of gas,” the NLP model might determine that the causal relationship is strong, but not as strong as the causal relationship between “The driver ran out of gas” and “The car broke down.”
3. Toxic Comment Classification:
4. Code Generation from Natural Language: Converting natural language descriptions into code (e.g., programming)
5. Commonsense Reasoning: Inferring and applying common-sense knowledge to text understanding
6. Coreference Resolution: Identifying when different words or expressions refer to the same entity
7. Cross-Lingual Transfer: Translating, transferring, or adapting NLP models across languages
8. Date Understanding:
   • Extracting dates from text: In this task, the NLP model is given a piece of text, and the model must extract all of the dates that are mentioned in the text. For example, given the text “The meeting will be held on March 8th, 2023, at 10:00 AM,” the NLP model should be able to extract the dates “March 8th, 2023” and “10:00 AM.”
   • Determining the meaning of dates: In this task, the NLP model is given a date, and the model must determine the meaning of the date. For example, given the date “March 8th, 2023,” the NLP model might determine that the date is a Tuesday.
   • Comparing dates: In this task, the NLP model is given two dates, and the model must determine which date is earlier or later. For example, given the dates “March 8th, 2023” and “March 9th, 2023,” the NLP model should be able to determine that March 8th, 2023 is earlier than March 9th, 2023.
9. Dependency Parsing: Analyzing grammatical structure and relationships between words in a sentence
10. Dialogue Management: Managing multi-turn conversations and interactions with users
11. Document Summarization: Condensing longer documents into shorter summaries
12. Fake News Detection: Identifying and classifying fake or misleading news articles
13. Formal Fallacies:
14. Humor and Sarcasm Detection: Identifying humorous or sarcastic content in text
15. Hyperbaton: Hyperbaton is a literary device that involves rearranging the normal word order of a sentence to create a more emphatic or expressive effect. It is often used in poetry and song lyrics to create a sense of rhythm or to highlight a particular word or phrase.
    • “Shall I compare thee to a summer’s day?” (Shakespeare, Sonnet 18)
    • Normal sentence can be “Shall I compare a summer’s to thee”
16. Language Detection and Identification: Detecting the language of a given text
17. Language Generation: It has tasks like Text Summarization, Text Generation (e.g., chatbots, content creation), Language Style Transfer
18. Language Modelling: This task involves predicting the next word in a sequence of words.
19. Legal and Medical Text Analysis: Analyzing legal documents or medical records for relevant information
20. Logical Reasoning:
    • Deductive reasoning: In deductive reasoning, the NLP model is given a set of premises, and the model must deduce a conclusion that follows logically from the premises. For example, given the premises “All cats are mammals” and “All mammals have four legs,” the NLP model must deduce the conclusion “All cats have four legs.”
    • Inductive reasoning: In inductive reasoning, the NLP model is given a set of observations, and the model must infer a general rule that explains the observations. For example, given the observations “John is tall”, “Mary is tall,” and “Peter is tall,” the NLP model might infer the rule “All people in this group are tall.”
    • Abductive reasoning: In abductive reasoning, the NLP model is given a set of observations and a hypothesis, and the model must determine whether the hypothesis is the best explanation for the observations. For example, given the observations “John is late for work” and “John’s car is in the shop,” the NLP model might abduce the hypothesis “John is taking the bus to work.”
21. Machine Translation: Translating text from one language to another
22. Math Word Problem Solving:
23. Morphological Analysis: Analyzing the structure and formation of words
24. Movie/Book/Service Recommendation:
25. Multi-task Language Understanding:
26. Multilingual NLP: Handling and processing text in multiple languages
27. Named Entity Recognition (NER): This tasks involves Identifying and classifying entities (e.g., names, locations, dates) in text
28. Natural Language Inference:
29. Navigate: Ability to understand the actions from the sentence like below.
    • Go to the kitchen and get me a glass of water.
    • Navigate to the nearest coffee shop.
    • Find the nearest exit.
    • Follow this path to the treasure chest.
    • Go one mile straight, then take uturn, move one 100 meters, take left turn, keep walking till dead end. On the left there is a building. Take a lift go to the 5th floor, flat number 506.
30. Part-of-Speech or PoS Tagging: It means tagging each word as noun, verb, adjective, adverb etc to words in a sentence.
31. Reasoning About Colored Objects (RACO): A simple RACO task might involve asking the NLP model to identify the color of an object, given a description of the object and its surroundings. A more complex RACO task might involve asking the NLP model to reason about the colors of multiple objects in a scene, and to predict the color of an object that is not explicitly mentioned in the text.
    • Identify the color of an object: Given the sentence “The red ball is on the table,” the NLP model should be able to identify the color of the ball as red.
    • Reason about the colors of multiple objects: Given the sentence “The red ball is on the table next to the green cup,” the NLP model should be able to reason about the colors of the ball and the cup, and to predict that the cup is green.
    • Predict the color of an object that is not explicitly mentioned in the text: Given the sentence “The red ball is on the table next to a cup,” the NLP model should be able to predict that the cup is green, even though the color of the cup is not explicitly mentioned in the text.
32. Ruin Names: In ethical or moral sense it looks bad but it is normal for human that when they want to take on somebody then instead of physical fighting they troll or call name. NLP can be used to generated hashtags with ruin names.
33. Sarcasm Detection: Sometimes when people want to give a serious feedback or troll someone then they use sarcastic langage. This task can identify whether a sentence is normal or sarcastic sentence.
34. Sentence Completion:
    • Fill in the blank: In this task, the NLP model is given a sentence with a blank, and the model must fill in the blank with the most likely word or phrase. For example, given the sentence “The cat sat on the __,” the NLP model might fill in the blank with the word “mat.”
    • Choose the best ending: In this task, the NLP model is given a sentence with two possible endings, and the model must choose the ending that is most likely to be correct. For example, given the sentence “The cat sat on the mat and __,” the NLP model might choose the ending “the dog chased it.”
    • Generate a new sentence: In this task, the NLP model is given a sentence, and the model must generate a new sentence that is similar in meaning to the original sentence. For example, given the sentence “The cat sat on the mat,” the NLP model might generate the sentence “The dog lay on the floor.”
35. Sentiment Analysis and Emotion Detection: Determining the sentiment or emotion expressed in a piece of text
36. SNARKS: Syntactic Neural Architecture for Recognizing Knowledge Structures. It involves following subtasks.
    • Semantic Role Labeling: Identifying the roles that different words or phrases play in a sentence, such as the subject, object, or modifier.
    • Syntax Parsing: Parsing sentences to identify the grammatical structure, dependencies between words, and syntactic relationships.
    • Information Extraction: Extracting specific information or knowledge from sentences, such as entities, facts, or events.
    • Knowledge Graph Construction: Building knowledge graphs by identifying entities, relationships, and attributes mentioned in text.
    • Coreference Resolution: Resolving references to the same entity across sentences or paragraphs.
    • Relation Extraction: Identifying relationships between entities mentioned in the text.
37. Speech-to-Text (STT) Transcription: Converting spoken language into written text
38. Sports Understanding:
39. Syllogisms Negation: Syllogism is a form of deductive reasoning that uses two premises to reach a conclusion. NLP can help in deducing the conclusion. Negation in syllogisms refers to the use of negative statements in the premises or conclusion. For example:
    • Premise 1: All cats are not dogs.
    • Premise 2: Fido is a dog.
    • Conclusion: Therefore, Fido is not a cat.
    • This conclusion is valid because it follows from the two premises.
40. Temporal Sequences: A temporal sequence task is a type of task that involves understanding and processing sequences of events that occur over time. Three primary temporal sequencing tasks are
    • Event detection: In event detection, the NLP model is given a text and must identify the events that occur in the text. The events can be simple, such as “Hari went to the store,” or they can be more complex, such as “Hari went to the store to buy milk.”
    • Event ordering: In event ordering, the NLP model is given a text and must identify the order in which the events occur. The order of events can be important for understanding the meaning of the text. For example, the sentence “Hari went to the store to buy milk” means something different than the sentence “Hari bought milk at the store.”
    • Event prediction: In event prediction, the NLP model is given a text and must predict the next event that will occur. This type of task is often used to predict the future or to generate creative text formats, such as poems or stories.
41. Text Analytics for Social Media and Customer Feedback: Analyzing social media posts, reviews, and customer feedback for insights
42. Text Classification and Categorization: It involves tasks like Sentiment Analysis (positive, negative, neutral), Topic Classification, Intent Detection, Document Classification, Spam Detection,
43. Text Completion and Prediction: Auto-completing sentences or predicting the next word in a sequence
44. Text Segmentation and Chunking: Dividing text into meaningful segments or chunks
45. Text Similarity and Semantic Search: Measuring semantic similarity between text documents or sentences. Conducting semantic search in large text corpora
46. Text Summarization:
47. Text-to-Speech (TTS) Synthesis: Converting written text into spoken language
48. Textual Entailment and Paraphrasing: Determining if one sentence logically follows from another, Generating paraphrased versions of sentences
49. Word Sense Disambiguation (WSD): WSD task involves determining the correct meaning of a word in a given context.
    • for example, the word “bank” can have multiple meanings, such as:
      • A financial institution that holds money
      • A sloping side of a hill or river
      • A row of trees or shrubs
      • A place where a river bends sharply
    • “I went to the bank to deposit my paycheck”. Bank=>financial institution
    • “The bank of the river is very steep”. Bank=> sloping side of a river
    • Three main applications of WSD are
      • Machine translation: WSD is important for machine translation because it allows the machine translation system to choose the correct translation for a word in a given context.
      • Question answering: WSD is important for question answering because it allows the question answering system to understand the meaning of the question and to find the correct answer.
      • Text summarization: WSD is important for text summarization because it allows the text summarization system to generate a summary that is accurate and relevant to the original text.

Question Answering Task

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50. Multiple Choice Question Answering (MCQA):
51. Disambiguation QA: Ambiguity arises in language when a word or phrase has multiple possible meanings or interpretations depending on the context. Disambiguation QA aims to overcome this challenge by correctly identifying the intended sense of the ambiguous terms in the question and selecting the most relevant answer accordingly.
    • Who is the king of Indian Poetry?
    • We need to understand “King” mean the greatest, famous, top. India has many languages, but the question is asked in English without mentioning name of the language there after disambiguation correct question is “Who is the most famous poet of Indian English poetry?”
52. Cross-Lingual Question Answering - XLQ: It involves answering questions in one language for which information is available in another language text. For example, the information about the capital of france is in French langague, but we ask the question in English langague. The answer is retrieved from French document and translated back to English. This task helps has applications like
    • Translation: Cross-lingual question answering can be used to improve the quality of machine translation. By understanding the meaning of the question in the source language, the NLP model can generate a more accurate translation of the question into the target language.
    • Search: Cross-lingual question answering can be used to improve the results of search engines. By understanding the meaning of the query in the user’s language, the NLP model can retrieve documents from multiple languages that are relevant to the query.
    • Customer support: Cross-lingual question answering can be used to provide customer support to users in multiple languages. By understanding the user’s question in their language, the NLP model can provide a more accurate and helpful response.
53. Penguins In A Table: This task helps us asking question in natural langague and getting answer from a table. For example we have table which contains information like penguin name, weight, age, beak length, species and we can ask following questions.
    • What is the average weight of the penguins?
    • What is the most common species of penguin?
    • What is the oldest penguin in the table?
    • What is the name of the penguin with the longest beak?
    • What is the total number of penguins in the table?
54. Question Answering (QA): Extractive QA ( Answering questions by selecting text spans from a document), Generative QA (Answering questions with coherent and relevant responses).

NLP Tasks with More Details

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Language Modeling

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There are many different types of language modeling tasks, but some of the most common include:

• Character-level language modeling: In character-level language modeling, the NLP model is given a sequence of characters and must predict the next character in the sequence.
• Word-level language modeling: In word-level language modeling, the NLP model is given a sequence of words and must predict the next word in the sequence.
• Sentence-level language modeling: In sentence-level language modeling, the NLP model is given a sequence of sentences and must predict the next sentence in the sequence.

There are many different approaches to language modeling, but some of the most common include:

• N-gram language models: N-gram language models are based on the principle that the probability of a word occurring in a sequence is dependent on the n-1 words that have already occurred in the sequence.
• Hidden Markov models: Hidden Markov models are statistical models that can be used to predict the next word in a sequence by modeling the probability of the next word given the previous words in the sequence.
• Recurrent neural networks: Recurrent neural networks are a type of deep learning model that can be used to predict the next word in a sequence by modeling the relationship between the previous words in the sequence and the next word.

Word Sense Disambiguation task

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There are many different approaches to WSD, but some of the most common include:

• Context-based approaches: These approaches use the context of the word to determine its meaning. For example, if the word “bank” is used in a sentence with the word “money,” then the context suggests that the word “bank” refers to a financial institution.
• Lexical knowledge-based approaches: These approaches use lexical knowledge, such as thesauri and dictionaries, to determine the meaning of a word. For example, if the word “bank” is used in a sentence with the word “river,” then the lexical knowledge-based approach could use a thesaurus to determine that the word “bank” refers to a sloping side of a river.
• Statistical approaches: These approaches use statistical methods to determine the meaning of a word. For example, the statistical approach could look at how the word “bank” has been used in the past to determine its most likely meaning in a given context.

Hyperbation

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There are a few different approaches to hyperbaton correction, but some of the most common include:

• Rule-based approaches: These approaches use a set of rules to identify hyperbaton and to correct it. For example, a rule-based approach could identify hyperbaton by looking for sentences that contain a verb that is followed by a noun phrase that is separated from the verb by another noun phrase.
• Statistical approaches: These approaches use statistical methods to identify hyperbaton and to correct it. For example, a statistical approach could look at how hyperbaton has been corrected in the past to determine the most likely correction for a given sentence.
• Neural network approaches: These approaches use neural networks to identify hyperbaton and to correct it. Neural networks are a type of machine learning model that can be used to learn the patterns of hyperbaton and to correct it accordingly.

New NLP Tasks

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• Datasets for New NLP Task
• Evaluation of New NLP Task

214 NLP Tasks from BIG Benchmark.

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As per BIG - (Beyound the Immitation Game) Benchmark there ar 214 NLP tasks. These tasks are more specific and extention of above list. More specific task helps in precise finetuning, validation and creating a better fined model.