ConTinTin: Continual Learning from Task Instructions Article Swipe
Wenjun Yin
,
Li, Jia
,
Caiming Xiong
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2022.acl-long.218
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2022.acl-long.218
The mainstream machine learning paradigms for NLP often work with two underlying presumptions. First, the target task is predefined and static; a system merely needs to learn to solve it exclusively. Second, the supervision of a task mainly comes from a set of labeled examples. A question arises: how to build a system that can keep learning new tasks from their instructions? This work defines a new learning paradigm ConTinTin (Continual Learning from Task Instructions), in which a system should learn a sequence of new tasks one by one, each task is explained by a piece of textual instruction. The system is required to (i) generate the expected outputs of a new task by learning from its instruction, (ii) transfer the knowledge acquired from upstream tasks to help solve downstream tasks (i.e., forward-transfer), and (iii) retain or even improve the performance on earlier tasks after learning new tasks (i.e., backward-transfer). This new problem is studied on a stream of more than 60 tasks, each equipped with an instruction. Technically, our method InstructionSpeak contains two strategies that make full use of task instructions to improve forward-transfer and backward-transfer: one is to learn from negative outputs, the other is to re-visit instructions of previous tasks. To our knowledge, this is the first time to study ConTinTin in NLP. In addition to the problem formulation and our promising approach, this work also contributes to providing rich analyses for the community to better understand this novel learning problem.
Related Topics
Concepts
Computer science
Task (project management)
Transfer of learning
Set (abstract data type)
Multi-task learning
Artificial intelligence
Sequence learning
Transfer (computing)
Inductive transfer
Machine learning
Human–computer interaction
Robot learning
Programming language
Management
Robot
Mobile robot
Economics
Parallel computing
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2022.acl-long.218
- https://aclanthology.org/2022.acl-long.218.pdf
- OA Status
- hybrid
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221144372
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221144372Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18653/v1/2022.acl-long.218Digital Object Identifier
- Title
-
ConTinTin: Continual Learning from Task InstructionsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Wenjun Yin, Li, Jia, Caiming XiongList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2022.acl-long.218Publisher landing page
- PDF URL
-
https://aclanthology.org/2022.acl-long.218.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://aclanthology.org/2022.acl-long.218.pdfDirect OA link when available
- Concepts
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Computer science, Task (project management), Transfer of learning, Set (abstract data type), Multi-task learning, Artificial intelligence, Sequence learning, Transfer (computing), Inductive transfer, Machine learning, Human–computer interaction, Robot learning, Programming language, Management, Robot, Mobile robot, Economics, Parallel computingTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.first | 209 |
| abstract_inverted_index.learn | 26, 80, 190 |
| abstract_inverted_index.needs | 24 |
| abstract_inverted_index.novel | 241 |
| abstract_inverted_index.often | 7 |
| abstract_inverted_index.other | 195 |
| abstract_inverted_index.piece | 95 |
| abstract_inverted_index.solve | 28, 128 |
| abstract_inverted_index.study | 212 |
| abstract_inverted_index.tasks | 58, 85, 125, 130, 143, 147 |
| abstract_inverted_index.their | 60 |
| abstract_inverted_index.which | 76 |
| abstract_inverted_index.(i.e., | 131, 148 |
| abstract_inverted_index.First, | 13 |
| abstract_inverted_index.better | 238 |
| abstract_inverted_index.mainly | 37 |
| abstract_inverted_index.merely | 23 |
| abstract_inverted_index.method | 170 |
| abstract_inverted_index.retain | 135 |
| abstract_inverted_index.should | 79 |
| abstract_inverted_index.stream | 157 |
| abstract_inverted_index.system | 22, 52, 78, 100 |
| abstract_inverted_index.target | 15 |
| abstract_inverted_index.tasks, | 162 |
| abstract_inverted_index.tasks. | 202 |
| abstract_inverted_index.Second, | 31 |
| abstract_inverted_index.arises: | 47 |
| abstract_inverted_index.defines | 64 |
| abstract_inverted_index.earlier | 142 |
| abstract_inverted_index.improve | 138, 183 |
| abstract_inverted_index.labeled | 43 |
| abstract_inverted_index.machine | 2 |
| abstract_inverted_index.outputs | 108 |
| abstract_inverted_index.problem | 152, 220 |
| abstract_inverted_index.static; | 20 |
| abstract_inverted_index.studied | 154 |
| abstract_inverted_index.textual | 97 |
| abstract_inverted_index.Learning | 71 |
| abstract_inverted_index.acquired | 122 |
| abstract_inverted_index.addition | 217 |
| abstract_inverted_index.analyses | 233 |
| abstract_inverted_index.contains | 172 |
| abstract_inverted_index.equipped | 164 |
| abstract_inverted_index.expected | 107 |
| abstract_inverted_index.generate | 105 |
| abstract_inverted_index.learning | 3, 56, 67, 114, 145, 242 |
| abstract_inverted_index.negative | 192 |
| abstract_inverted_index.outputs, | 193 |
| abstract_inverted_index.paradigm | 68 |
| abstract_inverted_index.previous | 201 |
| abstract_inverted_index.problem. | 243 |
| abstract_inverted_index.question | 46 |
| abstract_inverted_index.re-visit | 198 |
| abstract_inverted_index.required | 102 |
| abstract_inverted_index.sequence | 82 |
| abstract_inverted_index.transfer | 119 |
| abstract_inverted_index.upstream | 124 |
| abstract_inverted_index.ConTinTin | 69, 213 |
| abstract_inverted_index.approach, | 225 |
| abstract_inverted_index.community | 236 |
| abstract_inverted_index.examples. | 44 |
| abstract_inverted_index.explained | 92 |
| abstract_inverted_index.knowledge | 121 |
| abstract_inverted_index.paradigms | 4 |
| abstract_inverted_index.promising | 224 |
| abstract_inverted_index.providing | 231 |
| abstract_inverted_index.(Continual | 70 |
| abstract_inverted_index.downstream | 129 |
| abstract_inverted_index.knowledge, | 205 |
| abstract_inverted_index.mainstream | 1 |
| abstract_inverted_index.predefined | 18 |
| abstract_inverted_index.strategies | 174 |
| abstract_inverted_index.underlying | 11 |
| abstract_inverted_index.understand | 239 |
| abstract_inverted_index.contributes | 229 |
| abstract_inverted_index.formulation | 221 |
| abstract_inverted_index.performance | 140 |
| abstract_inverted_index.supervision | 33 |
| abstract_inverted_index.Technically, | 168 |
| abstract_inverted_index.exclusively. | 30 |
| abstract_inverted_index.instruction, | 117 |
| abstract_inverted_index.instruction. | 98, 167 |
| abstract_inverted_index.instructions | 181, 199 |
| abstract_inverted_index.instructions? | 61 |
| abstract_inverted_index.presumptions. | 12 |
| abstract_inverted_index.Instructions), | 74 |
| abstract_inverted_index.InstructionSpeak | 171 |
| abstract_inverted_index.forward-transfer | 184 |
| abstract_inverted_index.backward-transfer: | 186 |
| abstract_inverted_index.forward-transfer), | 132 |
| abstract_inverted_index.backward-transfer). | 149 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.0665362 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |