Self-study
Columnist: Support Computer voice-recognition technology currently cannot distinguish between homophones such as "their" and "there." As a consequence, Conclusion until voice-recognition technology is improved to recognize and utilize grammatical and semantic relations among words, voice-recognition programs will not accurately translate a computer user's spoken words into written text.
What's the Argument?
The columnist observes that voice-recognition technology can't tell homophones apart (words that sound the same but are spelled differently, like "their" and "there"). From this, she concludes that voice-recognition programs won't be able to translate spoken words into written text accurately until the technology is improved to recognize grammatical and semantic relations among words.
Note that by using the word "until," the columnist is saying that recognizing grammar and semantics is necessary for accurate translation. Not that it would be enough on its own, but that without it, accurate translation isn't happening.
The Missing Link
Let's start by identifying the premise and conclusion separately. Notice that the conclusion below is expressed as "can't recognize grammar/semantics → can't translate accurately" rather than "translate accurately → recognize grammar/semantics." These mean the same thing. The form below makes it easier to see how the premise connects to the conclusion through a chain.
Premise:
can't distinguish homophones
Conclusion:
can't recognize grammar/semantics
→
can't translate accurately
How does the premise connect to the conclusion? Through a chain. Here's the full chain the columnist needs:
can't recognize
grammar/semantics
→
grammar/semantics
can't distinguish
homophones
→
homophones
can't translate
accurately
accurately
missing — the assumption
implied by premise ✓
Right side of the chain (implied by premise)
The premise tells us the tech can't distinguish homophones. Although it doesn't spell out the consequence, it's implied: if the tech can't tell "their" from "there," it will get some words wrong, so it can't translate accurately. In other words, distinguishing homophones is a requirement for accurate translation.
Left side of the chain (missing — the assumption)
The columnist never explains why distinguishing homophones requires recognizing grammar and semantics. She just jumps from the homophone problem to grammar/semantics as the solution. Maybe the tech could distinguish homophones some other way, like analyzing word-frequency patterns. The correct answer should establish that grammar/semantics recognition is necessary for solving the homophone problem.
13.
Which one of the following ██ ██ ██████████ ████████ ██ ███ ███████████ █████████
a
In order for ████████ █████████████████ ██████████ ██ ███████████ ███████ ███████████ ██ ████ ██ ████ ██ █████████ ███████████ ███ ████████ █████████ █████ ██████
This fills the gap identified above. It says that in order to distinguish homophones, voice-recognition tech must be able to recognize grammatical and semantic relations. Without this assumption, the argument falls apart. If the technology could distinguish homophones through some other method, then the columnist would have no basis for claiming that grammar/semantics recognition is necessary for accurate translation. The argument can work only if recognizing grammar and semantics is required for solving the homophone problem.
b
If voice-recognition technology ██ ████████ ██ █████████ ███ ███████ ███████████ ███ ████████ █████████ █████ ██████ █████████████████ ████████ ████ ██████████ █████████ ██████ █████ ████ █████
This reverses the direction of the conclusion. The conclusion says that grammar/semantics recognition is necessary for accurate translation: without it, programs won't translate accurately. But (B) says grammar/semantics recognition is sufficient: if programs get it, they will translate accurately. That's a much stronger claim, and the argument doesn't need it. The columnist doesn't need to promise that fixing this one problem will solve everything. She only needs to establish that fixing this problem is required.
c
Humans can distinguish ███████ ██████████ ███████ █████ ███████████ █████ ███ ███████████ ███ ████████ █████████ █████ ██████
What humans can or can't do has no bearing on the columnist's reasoning. The argument is entirely about what voice-recognition technology needs in order to translate accurately. Even if humans distinguish homophones using completely different cognitive processes, that wouldn't affect whether the technology needs grammar/semantics recognition.
d
Unless voice-recognition technology ███ ███████████ ███████ █████ ████ ███ ███████████ ██ ██████ █████████ ███████████ ███ ████████ █████████ █████ ██████
This is the reverse of what we want. Compare (D) to (A):
(A): To distinguish homophones → must recognize grammar/semantics
(D): To recognize grammar/semantics → must distinguish homophones
Notice that the conclusion asserts recognizing grammar/semantics is required in order for something else (in order to translate accurately). But (D), restated, asserts that recognizing grammar/semantics is sufficient for distinguishing between homophones. This can’t be a necessary assumption, because the argument never claims that recognizing grammar/semantics is sufficient for something else.
e
Computer programs that █████ ███ ████████ ███ ███████ ██ ███████ ████ ███ █████████ ████ ██ ███████████ ███████ ███████████
The argument is about voice-recognition programs that convert speech to text. Programs that check spelling and grammar in already-written text are a completely different kind of software. What those programs can do has no relevance to what voice-recognition technology needs.