Some of the synonyms chosen are not really synonymous.
"Person is walking normally in a circle." turns into "Human is walking usually in a loop." But at best that's ungrammatical. At worst, it sounds like "usually" might modify "in a loop": that is, someone is spending most of their time walking in a loop, but some of their time walking in some other pattern.
"A human walks a quarter of a circle" turns into "A native motions a quarter of a loop". But "motions" as a verb can only refer to gesturing. I would expect to see someone waving their arm in a quarter circle.
But it probably doesn't matter. It sounds like the model's understanding of grammar (or at least its robustness to unusual sentence structures) is too weak for those nuances to even be relevant.
I agree with some of your points. Since the author is a non-native English speaker, there might be some grammatical issues in their English expressions. However, this is also constrained by the dataset; it's challenging to obtain sentences that are completely identical in both grammar and semantics. The author's main concern seems to be that when there are subtle semantic differences in inputs, the model shouldn't catastrophically fail. We can see examples like "Going ahead in an even pace," where previous models might even interpret it as moving backward. Or "A human utilizes his right arm to help himself to stand up," where the action of standing up might not even be present in other examples, posing serious problems. However, the author employs a similar approach to adversarial learning, enabling the model to learn expressions of actions that are similar to the original semantic sentences, which is already a significant improvement. We lack real motion data to learn expressions like "Going ahead in an even pace." The author also points out that there's a trade-off between stability and accuracy.
Comments
Some of the synonyms chosen are not really synonymous.
"Person is walking normally in a circle." turns into "Human is walking usually in a loop." But at best that's ungrammatical. At worst, it sounds like "usually" might modify "in a loop": that is, someone is spending most of their time walking in a loop, but some of their time walking in some other pattern.
"A human walks a quarter of a circle" turns into "A native motions a quarter of a loop". But "motions" as a verb can only refer to gesturing. I would expect to see someone waving their arm in a quarter circle.
But it probably doesn't matter. It sounds like the model's understanding of grammar (or at least its robustness to unusual sentence structures) is too weak for those nuances to even be relevant.
I agree with some of your points. Since the author is a non-native English speaker, there might be some grammatical issues in their English expressions. However, this is also constrained by the dataset; it's challenging to obtain sentences that are completely identical in both grammar and semantics. The author's main concern seems to be that when there are subtle semantic differences in inputs, the model shouldn't catastrophically fail. We can see examples like "Going ahead in an even pace," where previous models might even interpret it as moving backward. Or "A human utilizes his right arm to help himself to stand up," where the action of standing up might not even be present in other examples, posing serious problems. However, the author employs a similar approach to adversarial learning, enabling the model to learn expressions of actions that are similar to the original semantic sentences, which is already a significant improvement. We lack real motion data to learn expressions like "Going ahead in an even pace." The author also points out that there's a trade-off between stability and accuracy.