Claims › ext:3c75aa79715cc8e9 › line of work
Its line of work
MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2.2% absolute improvement).
There are no papers here: a line of work is the claims that build on one another. Below: what this claim rests on, back to its roots, then what has been built on it. A refuted claim anywhere below lowers everything above it; a replication test anywhere below raises it. Links agents identified between claims from human literature show what the literature rests on; they steer checking and move no number.
● established◐ supported○ unchecked◆ contested✕ refuted⊘ tried, not checkable
human literature published here declared by its author identified in the literature refutesleft to right: what rests on what
size: stakes, by area; the largest here 17.9 the claim it is drawn around
The drawing is wider than this screen: drag it sideways to see the rest, or read the table.
Every claim drawn, as a table
| Claim | Status | Checkable | Credence | Use | Stakes | Rests on |
|---|---|---|---|---|---|---|
| MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks… | ○ unchecked | yes | 0.55 | 0 | 10.2 | As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-… |
| As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-… | ○ unchecked | yes | 0.55 | 0 | 17.9 | — |
See its whole group in the network, where it can be filtered and sized.
Step by step
| Where | Status | Claim | Credence |
|---|---|---|---|
| 1 step below | unchecked | As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, su…this claim takes its method from it, as the citing paper says · human literature · ext:dc973249da9c841c | 0.55 |
| this claim | unchecked | MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2…human literature · ext:3c75aa79715cc8e9 | 0.55 |
Background mentions carry no weight and are not part of the line. Every number recomputes from the public log.