To produce example (2), the speaker needs to know only one Spanish word. But, to produce example (1), the speaker has to know what word order and case marker to use, and which languages they should be drawn from.

How languages are switched matters

1)  What a (multilingual) speaker is trying to achieve by switching languages can affect its structural outcome. Linguists recognize that pragmatic, interactional, and socio-indexical functions may condition C-S patterns.

2) Czech-English speakers switch to English for highinformation content words in prominent prosodic positions when speaking Czech

3) . Other functions, among these, quoting a speaker, getting the attention of an interlocutor, or reiterating an utterance to soften or intensify a message will also be indicated via C-S in predictable linguistic constructions

Why do speakers code-switch?

The contexts in which people acquire and use multiple languages in Europe are diverse. Some acquire their languages simultaneously from birth, while others acquire them sequentially, either naturally or via explicit instruction. Multilingualism is the norm in many zones where local residents may speak different languages to accommodate their interlocutors. Speakers who use local dialects or minoritized varieties may also be engaged in C-S when switching between their variety and a dominant on

C-S across Languages: European Context

C-S across Languages: Indian Context

The survey 'A Survey of Code-Switching' aims to inform researchers in computational linguistics (CL) and language technologies about the linguistic and social aspects of code-switching (C-S) in multilingual contexts, such as Europe and India. Additionally, it details how linguists describe and model these code-switching behaviors.

Goal of "A Survey of Code-switching"

inform code-switching in multilingual contexts

University of Michigan - Ann Arbor

NLG, a subfield of artificial intelligence (AI), is a software process that automatically transforms data into plain-English content.

Natural language generation

Autocorrection is an automatic text validation tool commonly found in your phone's and computer's text editing interfaces. This tool can correct spelling errors and/or contextual errors depending on the algorithms.

Autocorrection

The technique (Shannon, 1951) generates a word in a sequence given the previous words, using cross-entropy as the loss function and perplexity as the evaluation method. The sequence ends once it reaches a predefined length. Each softmax layer is to decide the next word based on the current hidden state in the RNN component. The initial input token(s) would depend on the NLP task's nature (e.g. machine translation, question answering, summarization). 

RNN's Approach to Text Generation in NLP (Autoregressive Generation)

Hämäläinen, M., & Alnajjar, K. (2021). Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers. arXiv preprint arXiv:2108.00308. https://arxiv.org/abs/2108.00308

Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers

- To present a comprehensive overview of NLG developments since 2000.
- To provide researchers with synthesis and pointers to relevant research.
- To introduce the field to researchers who are less familiar.

Goals of the NLG survey

- Content determination:  Deciding which information to include in the text under construction.
- Text structuring: Determining in which order information will be presented in the text.
- Sentence aggregation: Deciding which information to present in individual sentences.
- Lexicalisation:  Finding the right words and phrases to express information.
- Referring expression generation:  Selecting the words and phrases to identify domain objects.

NLG tasks.

GRL Book

A survey of embedding models of entities and relationships for knowledge graph completion

In Natural Language Processing, text generation tasks that use existing text as input can be broadly divided into two main categories. The first is text completion, which involves continuing to write based on the provided text. The second is text transformation, which focuses on converting the input text into a new, different text.

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