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This paper addresses the problem of how to weight task-specific losses in multi-task learning and presents an algorithm to adapt weights of loss functions, each of which corresponds to a task, during training.
The key idea is to update loss weights so that the weighted sum of task-specific gradients moves model paramet... | Does the review include a short summary of the paper? | yes |
1. The proposed method directly and naturally addresses the trade-off problem of multi-task learning. The design motivation is easy to understand thus can facilitate further, deeper research easily.
2. The presented experiments clearly demonstrated the performance superiority in two different types of text classificat... | Does the review include a short summary of the paper? | no |
This paper addresses the problem of how to weight task-specific losses in multi-task learning and presents an algorithm to adapt weights of loss functions, each of which corresponds to a task, during training.
The key idea is to update loss weights so that the weighted sum of task-specific gradients moves model paramet... | Does the review include a summary of the strengths of the paper? | yes |
This paper addresses the problem of how to weight task-specific losses in multi-task learning and presents an algorithm to adapt weights of loss functions, each of which corresponds to a task, during training.
The key idea is to update loss weights so that the weighted sum of task-specific gradients moves model paramet... | Does the review include a summary of the strengths of the paper? | no |
This paper addresses the problem of how to weight task-specific losses in multi-task learning and presents an algorithm to adapt weights of loss functions, each of which corresponds to a task, during training.
The key idea is to update loss weights so that the weighted sum of task-specific gradients moves model paramet... | Does the review include a summary of the weaknesses of the paper? | yes |
This paper addresses the problem of how to weight task-specific losses in multi-task learning and presents an algorithm to adapt weights of loss functions, each of which corresponds to a task, during training.
The key idea is to update loss weights so that the weighted sum of task-specific gradients moves model paramet... | Does the review include a summary of the weaknesses of the paper? | no |
This paper addresses the problem of how to weight task-specific losses in multi-task learning and presents an algorithm to adapt weights of loss functions, each of which corresponds to a task, during training.
The key idea is to update loss weights so that the weighted sum of task-specific gradients moves model paramet... | Does the review mention any comments, suggestions or typos that the author should address? | yes |
This paper addresses the problem of how to weight task-specific losses in multi-task learning and presents an algorithm to adapt weights of loss functions, each of which corresponds to a task, during training.
The key idea is to update loss weights so that the weighted sum of task-specific gradients moves model paramet... | Does the review mention any comments, suggestions or typos that the author should address? | no |
The authors propose to use static semi-factual generation + dynamic human-intervened correction to get better performance on OOD and few-shot performance. The method inlcudes 2 major steps: 1) use a rationale extraction model trained on small amount of annotated rationales to highlight rationales and replace non-ration... | Does the review include a short summary of the paper? | yes |
- The idea of using semi-factual generation is novel and interesting.
- Using raitonale model to expose model reasoning and ask human annotators for minimal effort (only identifies errors and leaves generating new examples to models) can largely reduce the annotation effort.
- The result that show better OOD performanc... | Does the review include a short summary of the paper? | no |
The authors propose to use static semi-factual generation + dynamic human-intervened correction to get better performance on OOD and few-shot performance. The method inlcudes 2 major steps: 1) use a rationale extraction model trained on small amount of annotated rationales to highlight rationales and replace non-ration... | Does the review include a summary of the strengths of the paper? | yes |
The authors propose to use static semi-factual generation + dynamic human-intervened correction to get better performance on OOD and few-shot performance. The method inlcudes 2 major steps: 1) use a rationale extraction model trained on small amount of annotated rationales to highlight rationales and replace non-ration... | Does the review include a summary of the strengths of the paper? | no |
The authors propose to use static semi-factual generation + dynamic human-intervened correction to get better performance on OOD and few-shot performance. The method inlcudes 2 major steps: 1) use a rationale extraction model trained on small amount of annotated rationales to highlight rationales and replace non-ration... | Does the review include a summary of the weaknesses of the paper? | yes |
The authors propose to use static semi-factual generation + dynamic human-intervened correction to get better performance on OOD and few-shot performance. The method inlcudes 2 major steps: 1) use a rationale extraction model trained on small amount of annotated rationales to highlight rationales and replace non-ration... | Does the review include a summary of the weaknesses of the paper? | no |
The authors propose to use static semi-factual generation + dynamic human-intervened correction to get better performance on OOD and few-shot performance. The method inlcudes 2 major steps: 1) use a rationale extraction model trained on small amount of annotated rationales to highlight rationales and replace non-ration... | Does the review mention any comments, suggestions or typos that the author should address? | yes |
The authors propose to use static semi-factual generation + dynamic human-intervened correction to get better performance on OOD and few-shot performance. The method inlcudes 2 major steps: 1) use a rationale extraction model trained on small amount of annotated rationales to highlight rationales and replace non-ration... | Does the review mention any comments, suggestions or typos that the author should address? | no |
The paper presents a rational-centric framework with human-in-the-loop to boost model out-of-distribution performance in few-shot learning scenarios. The proposed approach uses static semi-factual generation and human corrections to decouple spurious associations and bias models towards generally applicable underlying ... | Does the review include a short summary of the paper? | yes |
- Improving out-of-distribution model performance specially in few-shot learning settings is an important problem of real-life need in the NLP community.
- The proposed approach is simple and can be applied for NLP tasks where rationales can be easily identified and annotated. For instance, in sentiment analysis and te... | Does the review include a short summary of the paper? | no |
The paper presents a rational-centric framework with human-in-the-loop to boost model out-of-distribution performance in few-shot learning scenarios. The proposed approach uses static semi-factual generation and human corrections to decouple spurious associations and bias models towards generally applicable underlying ... | Does the review include a summary of the strengths of the paper? | yes |
The paper presents a rational-centric framework with human-in-the-loop to boost model out-of-distribution performance in few-shot learning scenarios. The proposed approach uses static semi-factual generation and human corrections to decouple spurious associations and bias models towards generally applicable underlying ... | Does the review include a summary of the strengths of the paper? | no |
The paper presents a rational-centric framework with human-in-the-loop to boost model out-of-distribution performance in few-shot learning scenarios. The proposed approach uses static semi-factual generation and human corrections to decouple spurious associations and bias models towards generally applicable underlying ... | Does the review include a summary of the weaknesses of the paper? | yes |
The paper presents a rational-centric framework with human-in-the-loop to boost model out-of-distribution performance in few-shot learning scenarios. The proposed approach uses static semi-factual generation and human corrections to decouple spurious associations and bias models towards generally applicable underlying ... | Does the review include a summary of the weaknesses of the paper? | no |
The paper presents a rational-centric framework with human-in-the-loop to boost model out-of-distribution performance in few-shot learning scenarios. The proposed approach uses static semi-factual generation and human corrections to decouple spurious associations and bias models towards generally applicable underlying ... | Does the review mention any comments, suggestions or typos that the author should address? | yes |
The paper presents a rational-centric framework with human-in-the-loop to boost model out-of-distribution performance in few-shot learning scenarios. The proposed approach uses static semi-factual generation and human corrections to decouple spurious associations and bias models towards generally applicable underlying ... | Does the review mention any comments, suggestions or typos that the author should address? | no |
This paper proposes, as a form of data augmentation, to replace the one-hot sequences that are typically consumed by text classification models with an interpoloation between this one-hot distribution and a distribution over word-types obtained by running the sequence through BERT. The authors show that this form of au... | Does the review include a short summary of the paper? | yes |
- The paper obtains good results with a straightforward approach.
- The paper is written fairly clearly.
- The paper needs some light editing (especially Section 4.1) but I don't see any significant weaknesses.
- One thing I wasn't sure I understood is whether the "smoothed" inputs are used on their own to train the ... | Does the review include a short summary of the paper? | no |
This paper proposes, as a form of data augmentation, to replace the one-hot sequences that are typically consumed by text classification models with an interpoloation between this one-hot distribution and a distribution over word-types obtained by running the sequence through BERT. The authors show that this form of au... | Does the review include a summary of the strengths of the paper? | yes |
This paper proposes, as a form of data augmentation, to replace the one-hot sequences that are typically consumed by text classification models with an interpoloation between this one-hot distribution and a distribution over word-types obtained by running the sequence through BERT. The authors show that this form of au... | Does the review include a summary of the strengths of the paper? | no |
This paper proposes, as a form of data augmentation, to replace the one-hot sequences that are typically consumed by text classification models with an interpoloation between this one-hot distribution and a distribution over word-types obtained by running the sequence through BERT. The authors show that this form of au... | Does the review include a summary of the weaknesses of the paper? | yes |
This paper proposes, as a form of data augmentation, to replace the one-hot sequences that are typically consumed by text classification models with an interpoloation between this one-hot distribution and a distribution over word-types obtained by running the sequence through BERT. The authors show that this form of au... | Does the review include a summary of the weaknesses of the paper? | no |
This paper proposes, as a form of data augmentation, to replace the one-hot sequences that are typically consumed by text classification models with an interpoloation between this one-hot distribution and a distribution over word-types obtained by running the sequence through BERT. The authors show that this form of au... | Does the review mention any comments, suggestions or typos that the author should address? | yes |
This paper proposes, as a form of data augmentation, to replace the one-hot sequences that are typically consumed by text classification models with an interpoloation between this one-hot distribution and a distribution over word-types obtained by running the sequence through BERT. The authors show that this form of au... | Does the review mention any comments, suggestions or typos that the author should address? | no |
The paper proposes a data augmentation method using a controllable smoothed representation, which is obtained by combining the one-hot representation and the smooth representation through masked language modeling. The authors showed the effectiveness of the proposed method on low-resourced sentence classification task.... | Does the review include a short summary of the paper? | yes |
- The paper is well-structured. The authors explained the motivation and the methodology clearly. Figure 1 and 2 are informative and help the readers understand better understand the method.
- It is nice that the text smoothing can be combined with other data augmentation approaches to achieve better performances.
- T... | Does the review include a short summary of the paper? | no |
The paper proposes a data augmentation method using a controllable smoothed representation, which is obtained by combining the one-hot representation and the smooth representation through masked language modeling. The authors showed the effectiveness of the proposed method on low-resourced sentence classification task.... | Does the review include a summary of the strengths of the paper? | yes |
The paper proposes a data augmentation method using a controllable smoothed representation, which is obtained by combining the one-hot representation and the smooth representation through masked language modeling. The authors showed the effectiveness of the proposed method on low-resourced sentence classification task.... | Does the review include a summary of the strengths of the paper? | no |
The paper proposes a data augmentation method using a controllable smoothed representation, which is obtained by combining the one-hot representation and the smooth representation through masked language modeling. The authors showed the effectiveness of the proposed method on low-resourced sentence classification task.... | Does the review include a summary of the weaknesses of the paper? | yes |
The paper proposes a data augmentation method using a controllable smoothed representation, which is obtained by combining the one-hot representation and the smooth representation through masked language modeling. The authors showed the effectiveness of the proposed method on low-resourced sentence classification task.... | Does the review include a summary of the weaknesses of the paper? | no |
The paper proposes a data augmentation method using a controllable smoothed representation, which is obtained by combining the one-hot representation and the smooth representation through masked language modeling. The authors showed the effectiveness of the proposed method on low-resourced sentence classification task.... | Does the review mention any comments, suggestions or typos that the author should address? | yes |
The paper proposes a data augmentation method using a controllable smoothed representation, which is obtained by combining the one-hot representation and the smooth representation through masked language modeling. The authors showed the effectiveness of the proposed method on low-resourced sentence classification task.... | Does the review mention any comments, suggestions or typos that the author should address? | no |
The paper introduces a text smoothing approach and uses it in different downstream tasks. Different types of sentence classification tasks are improved by using such text smoothing method.
The paper demonstrate improvement from using proposed text smoothing method.
This paper does not make a significant contribution.... | Does the review include a short summary of the paper? | yes |
The paper demonstrate improvement from using proposed text smoothing method.
This paper does not make a significant contribution. Smoothed Representation and Mixup Strategy are not proposed by the authors. Moreover, the strategy is too simple to combine the one-hot representation and smoothed representation with a wei... | Does the review include a short summary of the paper? | no |
The paper introduces a text smoothing approach and uses it in different downstream tasks. Different types of sentence classification tasks are improved by using such text smoothing method.
The paper demonstrate improvement from using proposed text smoothing method.
This paper does not make a significant contribution.... | Does the review include a summary of the strengths of the paper? | yes |
The paper introduces a text smoothing approach and uses it in different downstream tasks. Different types of sentence classification tasks are improved by using such text smoothing method.
This paper does not make a significant contribution. Smoothed Representation and Mixup Strategy are not proposed by the authors. M... | Does the review include a summary of the strengths of the paper? | no |
The paper introduces a text smoothing approach and uses it in different downstream tasks. Different types of sentence classification tasks are improved by using such text smoothing method.
The paper demonstrate improvement from using proposed text smoothing method.
This paper does not make a significant contribution.... | Does the review include a summary of the weaknesses of the paper? | yes |
The paper introduces a text smoothing approach and uses it in different downstream tasks. Different types of sentence classification tasks are improved by using such text smoothing method.
The paper demonstrate improvement from using proposed text smoothing method.
One of the biggest problems is that motivation is no... | Does the review include a summary of the weaknesses of the paper? | no |
The paper introduces a text smoothing approach and uses it in different downstream tasks. Different types of sentence classification tasks are improved by using such text smoothing method.
The paper demonstrate improvement from using proposed text smoothing method.
This paper does not make a significant contribution.... | Does the review mention any comments, suggestions or typos that the author should address? | yes |
The paper introduces a text smoothing approach and uses it in different downstream tasks. Different types of sentence classification tasks are improved by using such text smoothing method.
The paper demonstrate improvement from using proposed text smoothing method.
This paper does not make a significant contribution.... | Does the review mention any comments, suggestions or typos that the author should address? | no |
This paper proposes a data augmentation technique named Text Smoothing, which converts sentences from their one-hot representations to controllable smoothed representations. Specifically, the authors multiply the output of a pre-trained BERT with the word embedding matrix to get the smoothed representation of an input ... | Does the review include a short summary of the paper? | yes |
__1. The paper is well organized and easy to follow.__
__2. The proposed method is novel and interesting:__ The idea of mixing the one-hot representation and the LM (smoothed) representation for an input token is very different from other works of data augmentation. It uses the knowledge of pre-trained BERT to integrat... | Does the review include a short summary of the paper? | no |
This paper proposes a data augmentation technique named Text Smoothing, which converts sentences from their one-hot representations to controllable smoothed representations. Specifically, the authors multiply the output of a pre-trained BERT with the word embedding matrix to get the smoothed representation of an input ... | Does the review include a summary of the strengths of the paper? | yes |
This paper proposes a data augmentation technique named Text Smoothing, which converts sentences from their one-hot representations to controllable smoothed representations. Specifically, the authors multiply the output of a pre-trained BERT with the word embedding matrix to get the smoothed representation of an input ... | Does the review include a summary of the strengths of the paper? | no |
This paper proposes a data augmentation technique named Text Smoothing, which converts sentences from their one-hot representations to controllable smoothed representations. Specifically, the authors multiply the output of a pre-trained BERT with the word embedding matrix to get the smoothed representation of an input ... | Does the review include a summary of the weaknesses of the paper? | yes |
This paper proposes a data augmentation technique named Text Smoothing, which converts sentences from their one-hot representations to controllable smoothed representations. Specifically, the authors multiply the output of a pre-trained BERT with the word embedding matrix to get the smoothed representation of an input ... | Does the review include a summary of the weaknesses of the paper? | no |
This paper proposes a data augmentation technique named Text Smoothing, which converts sentences from their one-hot representations to controllable smoothed representations. Specifically, the authors multiply the output of a pre-trained BERT with the word embedding matrix to get the smoothed representation of an input ... | Does the review mention any comments, suggestions or typos that the author should address? | yes |
This paper proposes a data augmentation technique named Text Smoothing, which converts sentences from their one-hot representations to controllable smoothed representations. Specifically, the authors multiply the output of a pre-trained BERT with the word embedding matrix to get the smoothed representation of an input ... | Does the review mention any comments, suggestions or typos that the author should address? | no |
This paper proposes a framework for training and extract-the-generate model for the long document summarization task. The premise is that the extractor should pass on important information from the source to the generator for producing abstractive summaries. To train the entire network, there have been three losses def... | Does the review include a short summary of the paper? | yes |
-The paper is easy-to-follow and understandable in most parts.
-The problem is well-approached, although it has not been motivated much in the paper.
-Results outperform the prior SOTA by a large margin on two long datasets (GovReport and QMSum).
-The idea makes sense for the long document summarization, but I’m won... | Does the review include a short summary of the paper? | no |
This paper proposes a framework for training and extract-the-generate model for the long document summarization task. The premise is that the extractor should pass on important information from the source to the generator for producing abstractive summaries. To train the entire network, there have been three losses def... | Does the review include a summary of the strengths of the paper? | yes |
This paper proposes a framework for training and extract-the-generate model for the long document summarization task. The premise is that the extractor should pass on important information from the source to the generator for producing abstractive summaries. To train the entire network, there have been three losses def... | Does the review include a summary of the strengths of the paper? | no |
This paper proposes a framework for training and extract-the-generate model for the long document summarization task. The premise is that the extractor should pass on important information from the source to the generator for producing abstractive summaries. To train the entire network, there have been three losses def... | Does the review include a summary of the weaknesses of the paper? | yes |
This paper proposes a framework for training and extract-the-generate model for the long document summarization task. The premise is that the extractor should pass on important information from the source to the generator for producing abstractive summaries. To train the entire network, there have been three losses def... | Does the review include a summary of the weaknesses of the paper? | no |
This paper proposes a framework for training and extract-the-generate model for the long document summarization task. The premise is that the extractor should pass on important information from the source to the generator for producing abstractive summaries. To train the entire network, there have been three losses def... | Does the review mention any comments, suggestions or typos that the author should address? | yes |
This paper proposes a framework for training and extract-the-generate model for the long document summarization task. The premise is that the extractor should pass on important information from the source to the generator for producing abstractive summaries. To train the entire network, there have been three losses def... | Does the review mention any comments, suggestions or typos that the author should address? | no |
- This paper works on zero-shot relation extraction. Note that the authors assume that ground-truth entities in each sentence are given as input. In other words, this task does not require identifying entities.
- The problematic issue this paper focuses on is the difficulty of distinguishing similar but different-class... | Does the review include a short summary of the paper? | yes |
- Their method consistently achieves performance improvements (Table 2). - Their proposed method is so simple that readers can reimplement their method.
- Their claim is not properly supported. In Section 1 (in line 71-84 and 121-124), the authors introduce the problem they want to solve, and they state as follows: “... | Does the review include a short summary of the paper? | no |
- This paper works on zero-shot relation extraction. Note that the authors assume that ground-truth entities in each sentence are given as input. In other words, this task does not require identifying entities.
- The problematic issue this paper focuses on is the difficulty of distinguishing similar but different-class... | Does the review include a summary of the strengths of the paper? | yes |
- This paper works on zero-shot relation extraction. Note that the authors assume that ground-truth entities in each sentence are given as input. In other words, this task does not require identifying entities.
- The problematic issue this paper focuses on is the difficulty of distinguishing similar but different-class... | Does the review include a summary of the strengths of the paper? | no |
- This paper works on zero-shot relation extraction. Note that the authors assume that ground-truth entities in each sentence are given as input. In other words, this task does not require identifying entities.
- The problematic issue this paper focuses on is the difficulty of distinguishing similar but different-class... | Does the review include a summary of the weaknesses of the paper? | yes |
- This paper works on zero-shot relation extraction. Note that the authors assume that ground-truth entities in each sentence are given as input. In other words, this task does not require identifying entities.
- The problematic issue this paper focuses on is the difficulty of distinguishing similar but different-class... | Does the review include a summary of the weaknesses of the paper? | no |
- This paper works on zero-shot relation extraction. Note that the authors assume that ground-truth entities in each sentence are given as input. In other words, this task does not require identifying entities.
- The problematic issue this paper focuses on is the difficulty of distinguishing similar but different-class... | Does the review mention any comments, suggestions or typos that the author should address? | yes |
- This paper works on zero-shot relation extraction. Note that the authors assume that ground-truth entities in each sentence are given as input. In other words, this task does not require identifying entities.
- The problematic issue this paper focuses on is the difficulty of distinguishing similar but different-class... | Does the review mention any comments, suggestions or typos that the author should address? | no |
This paper presents several logic failures in the way that attribution methods are evaluated. For each failure they identify, the authors discuss why it will prevent proper identification of attribution quality, and design experiments that crystallize this potential failure. The authors survey the attribution evaluatio... | Does the review include a short summary of the paper? | yes |
This is a well written paper that discusses an important and very timely subject. It highlights important failures that are often overlooked in the model interpretability literature. Each failure the authors identify is clearly explained and motivated, and the experiments that demonstrate it are neat and clear. I belie... | Does the review include a short summary of the paper? | no |
This paper presents several logic failures in the way that attribution methods are evaluated. For each failure they identify, the authors discuss why it will prevent proper identification of attribution quality, and design experiments that crystallize this potential failure. The authors survey the attribution evaluatio... | Does the review include a summary of the strengths of the paper? | yes |
This paper presents several logic failures in the way that attribution methods are evaluated. For each failure they identify, the authors discuss why it will prevent proper identification of attribution quality, and design experiments that crystallize this potential failure. The authors survey the attribution evaluatio... | Does the review include a summary of the strengths of the paper? | no |
This paper presents several logic failures in the way that attribution methods are evaluated. For each failure they identify, the authors discuss why it will prevent proper identification of attribution quality, and design experiments that crystallize this potential failure. The authors survey the attribution evaluatio... | Does the review include a summary of the weaknesses of the paper? | yes |
This paper presents several logic failures in the way that attribution methods are evaluated. For each failure they identify, the authors discuss why it will prevent proper identification of attribution quality, and design experiments that crystallize this potential failure. The authors survey the attribution evaluatio... | Does the review include a summary of the weaknesses of the paper? | no |
This paper presents several logic failures in the way that attribution methods are evaluated. For each failure they identify, the authors discuss why it will prevent proper identification of attribution quality, and design experiments that crystallize this potential failure. The authors survey the attribution evaluatio... | Does the review mention any comments, suggestions or typos that the author should address? | yes |
This paper presents several logic failures in the way that attribution methods are evaluated. For each failure they identify, the authors discuss why it will prevent proper identification of attribution quality, and design experiments that crystallize this potential failure. The authors survey the attribution evaluatio... | Does the review mention any comments, suggestions or typos that the author should address? | no |
The paper uncovers critical issues around interpretability and explainability of the reasoning and performance of modern deep learning models. Using several theoretical and experimental analysis, it specifically reveals the weaknesses of existing attribution methods designed to qualify and support research propositions... | Does the review include a short summary of the paper? | yes |
Precise and concise abstract.
The paper is organised and well written.
The subject addressed is crucial for the deep learning community. Redirecting attention to the appropriately selecting attribution methods (during research task evaluation stages) can subtly reduce the immense effort and focus researchers have in ou... | Does the review include a short summary of the paper? | no |
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