1200 English Short Phrases For Conversation

[Mirtha Shikles]

I hear a lot of conflicting advice on how to write sentences, and I bet you do too. Should you write short sentences, because they are easier to read? Should you write longer sentences because they sound more academic? Should you write a careful mix of sentences, because that creates good flow?

Academic writing is often talking about information that is complex, indefinite or super-specific: in other words, it is not simple, short and straightforward. Simple, short, straightforward sentences might cut out much of the information that you need in order to be correct.

Shorter sentences are less information dense than longer sentences, so they may not work as well when you have a lot of information to convey but only a limited number of words. Every sentence needs a subject (noun that the sentence is about), and a main verb (what the subject is doing), and you need to repeat this for every new sentence. A lot of short sentences therefore use up valuable words in abstracts and grant applications, especially if you only have 100 characters or 250 words!

Other scholars often write in longer sentences. So you may want to write like them, to show you belong. If you are in a discipline where you regularly quote the work of other researchers, the quotes are likely to be long too.

Longer sentences allow you to cram a lot of information into a small number of words. Many of the longest sentences I find in journal articles are being very efficient with words. List sentences, for example, might be 50+ words long, but allow the writer to cover a lot of ground with fewer words. Or a long sentence in a literature review might summarise a big argument, to show that the author is familiar with the ideas and contribution of a body of scholarship, without getting distracted from the main topic.

Long sentences are often needed when explaining complex theories, introducing uncertain or incomplete knowledge, or mobilising criticism of important scholars or common knowledge. Nuance, hedging, politeness and detail all require more words in the sentence.

A typical academic sentence is 25-35 words, and has a main clause and 1-2 subordinate clauses. (Subordinate clauses are those extra phrases you add in, perhaps separated by commas or brackets).

The auto speech adaptation feature improves the speech recognition accuracy ofyour agent by automatically using conversation state to pass relevant entitiesand training phrases as speech context hints for all detect intent requests.This feature isdisabledby default.

With auto speech adaptation enabled, you can build your agent in ways to takeadvantage of it. The following sections explain how speech recognition may beimproved with certain changes to your agent's training phrases,and entities.

In all cases, auto speech adaptation is only biasing the speech recognition, notlimiting it. For example, even if Dialogflow is prompting a user for a requiredparameter, users will still be able to trigger other intents such as atop-level "talk to an agent" intent.

If you define acustom entityfor product or service names offered by your company,and the end-user mentions these terms in an utterance, they are more likelyto be recognized. A training phrase "I love Dialogflow", where "Dialogflow"is annotated as the @product entity, will tell auto speech adaptation tobias for "I love Dialogflow", "I love Cloud Speech", and all other entriesin the @product entity.

It is especially important to define clean entity synonyms when usingDialogflow to detect speech. Imagine you have two @product entity entries,"Dialogflow" and "Dataflow". Your synonyms for "Dialogflow" might be"Dialogflow", "dialogue flow", "dialogue builder","Speaktoit", "speak to it", "API.ai", "API dot AI".These are good synonyms because they cover the most common variations. Youdon't need to add "the dialogue flow builder" because "dialogue flow"already covers that.

While any regular expression can be used to extract entities from text inputs,only certain expressions will tell auto speech adaptation to bias forspelled-out alphanumeric or digit sequences when recognizing speech.

Mark the regexp entity as a requiredform parameter,so it can be collected duringform filling.This allows auto speech adaptation tostrongly bias for sequence recognition instead of trying to recognize anintent and sequence at the same time. Otherwise, "Where is my package forABC123" might be misrecognized as "Where is my package 4ABC123".

For example, a regexp entity with a single entry ([a-zA-Z0-9]\s?)5,9 willnot trigger the speech sequence recognizer because it contains a capture group.To fix this, simply add another entry for [a-zA-Z0-9]5,9. Now you willbenefit from the sequence recognizer when matching "ABC123",yet the NLU will still match inputs like "ABC 123" thanks to the originalrule that allows spaces.

If your language is not listed, you can work around this limitation.For example, if you want an employee ID that isthree letters followed by three digits to be accurately recognized, you couldbuild your agent with the following entities and parameters:

Manual speech adaptation allows you to manually configure speech adaptation phrasesfor a flow or a page. It also overrides implicit speech contexts generated byauto speech adaptation when the latter is enabled.

The flow level and page level speech adaptation settings have a hierarchicalrelation, which means that a page inherits speech adaptation settings fromthe flow level by default and the more fine-grained page level alwaysoverrides flow level if the page has a customized setting.

For speech adaptation setting, flow level setting and page level settingcan be enabled independently.If the flow level adaptation setting is not enabled,you can still choose Customize at page level to enablemanual speech adaptation for that specific page. Similarly, if you disablemanual speech adaptation in flow level setting, pages in the flow withCustomize selected will not be impacted.

However, flow level setting and page level setting cannot be disabled independently.If a flow has manual speech adaptation enabled, you cannot disable it for apage under the flow through the Customize choice. Therefore, if you want tohave a mixed usage of manual speech adaptation and auto speech adaptation for pageswithin a flow, you should not enable manual speech adaptation at flow level andshould only use page level adaptation settings instead. You can refer to the tablebelow to understand what combination of flow and page setting you should usefor your case of adaptation.

In an adaptation phrase set, you can define single-word or multi-word phraseswith optional references to speech class tokens. For example, you can addphrases like "great rate", "tracking number is $OOV_CLASS_ALPHANUMERIC_SEQUENCE", or "$FULLPHONENUM".These provided phrases increase the probability of them getting transcribed overother phonetically similar phrases. When you add a multi-word phrase without anyboost, the bias is applied to both the whole phrase andthe continuous portions within the phrase. In general, the number of phrasesshould be kept small and you should only add phrases that the speech recognitionstruggles to get right without speech adaptation. If Speech-to-Text can alreadyrecognize a phrase correctly, then there's no need to add this phrase intospeech adaptation settings. If you see a few phrases that Speech-to-Text oftenmisrecognizes at a page or flow, you can add the correct phrases to itscorresponding adaptation settings.

Here's an example of how you can use speech adaptation to correct recognitionissues. Let's say you are designing a phone device trading agent, andthe user may either say something including the phrases "sell phones" or"cell phone" after the agent asks its first question "what do you need help with?".Then how can we use speech adaptation to improve recognition accuracy on both phrases?

If you include both phrases in the adaptation settings,Speech-to-Text may still be confused, as they sound similar.If you just provide one phrase out of the two, then Speech-to-Text maymisrecognize one phrase as the other. To improve speech recognition accuracyfor both phrases, you need to provide Speech-to-Text with more context clues todistinguish between when it should hear "sell phones" andwhen it should hear "cell phone". For example,you may notice people often use "sell phones" as a part of utterances like"how to sell phones", "want to sell phones" or "do you sell phones", whereas"cell phone" as a part of utterances like "purchase cell phone", "cell phone bill",and "cell phone service". If you provide these more precise phrases to the modelinstead of the short original phrases "cell phone" and "sell phones", Speech-to-Textwill learn that "sell phone" as a verb phrase is more likely to follow after wordslike "how to", "want to" and "do you", while "cell phone" as a nounphrase is more likely to follow after words like "purchase" or be followed by words like "bill" or "service". Therefore, as a rule of thumb for configuringadaptation phrases, it is usually better to provide more precise phrases like"how to sell phones" or "do you sell phones" than only including "sell phone".

Apart from natural language words, you can also embed references tospeech class tokens into a phrase. Speech class tokensrepresent common concepts that usually follow certainformat in writing. For example, for the address number in an address like"123 Main Street", people would usually expect to see an address number'snumerical format "123" in an address instead of its fully spelled-out version"one-hundred twenty-three". If you expectcertain formatting in the transcription results, especially for alphanumericsequences, please refer to thelist of supported class tokensto see which tokens are available for your language and your use case.

Boost applies additional bias when set to values greater than 0 and no more than20. When boost is empty or 0, the default biasing effect helps recognize thewhole phrase and the continuous portions within the phrase. For example, anon-boosted phrase "are you open to sell phones" helps recognize that phraseand also similar phrases like "I sell phones" and "Hi are you open".

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