Identifying and Controlling Important Neurons in Neural Machine Translation
Neural machine translation (NMT) models learn representations containing substantial linguistic information. However, it is not clear if such information is fully distributed or if some of it can be attributed to individual neurons. We develop unsupervised methods for discovering important neurons in NMT models. Our methods rely on the intuition that different models learn similar properties, and do not require any costly external supervision. We show experimentally that translation quality depends on the discovered neurons, and find that many of them capture common linguistic phenomena. Finally, we show how to control NMT translations in predictable ways, by modifying activations of individual neurons.
 This paper presents unsupervised approaches to discover import neurons in neural machine translation systems. Some linguistic properties controlled by the discovered neurons are discussed and analyzed. Strengths: The paper is well-written and provides valuable information to understand the behaviour of neural machine translation models. The ability to control characteristics (such as gender) without training specialized models is promising, even if the results are not good enough for immediate use. It would be interesting to see whether controlling neurons in the decoder would be more effective. Weaknesses: Multiple NMT systems are necessary to discover important neurons. The authors mention that it would be possible to use different checkpoints from a single model, but don*t evaluate how well this would work. The findings in this paper do not lead to immediate translation performance improvements. Questions and other remarks: In Table 4a, why are there 2 results for "-0.25, -0.125, 0"? In section 4.3 (Tense), it may be worthwhile to mention that the neuron is highly activated on the word "Spreads", even if it acts as a noun in this specific sentence. Bottom of p. 6: "Our supervised methods" -> "Our unsupervised methods" To control properties, could SVCCA directions or coefficients be manipulated? Some parentheses around citations are missing or misplaced.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Identifying and Controlling Important Neurons in Neural Machine Translation
Neural machine translation (NMT) models learn representations containing substantial linguistic information. However, it is not clear if such information is fully distributed or if some of it can be attributed to individual neurons. We develop unsupervised methods for discovering important neurons in NMT models. Our methods rely on the intuition that different models learn similar properties, and do not require any costly external supervision. We show experimentally that translation quality depends on the discovered neurons, and find that many of them capture common linguistic phenomena. Finally, we show how to control NMT translations in predictable ways, by modifying activations of individual neurons.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper presents unsupervised approaches to discover import neurons in neural machine translation systems. Some linguistic properties controlled by the discovered neurons are discussed and analyzed. Strengths: The paper is well-written and provides valuable information to understand the behaviour of neural machine translation models. The ability to control characteristics (such as gender) without training specialized models is promising, even if the results are not good enough for immediate use. It would be interesting to see whether controlling neurons in the decoder would be more effective. Weaknesses: Multiple NMT systems are necessary to discover important neurons. The authors mention that it would be possible to use different checkpoints from a single model, but don*t evaluate how well this would work. The findings in this paper do not lead to immediate translation performance improvements. Questions and other remarks: In Table 4a, why are there 2 results for "-0.25, -0.125, 0"? In section 4.3 (Tense), it may be worthwhile to mention that the neuron is highly activated on the word "Spreads", even if it acts as a noun in this specific sentence. Bottom of p. 6: "Our supervised methods" -> "Our unsupervised methods" To control properties, could SVCCA directions or coefficients be manipulated? Some parentheses around citations are missing or misplaced.
assistant
 This paper presents unsupervised approaches to discover import neurons in neural machine translation systems. Some linguistic properties controlled by the discovered neurons are discussed and analyzed. Strengths: The paper is well-written and provides valuable information to understand the behaviour of neural machine translation models. The ability to control characteristics (such as gender) without training specialized models is promising, even if the results are not good enough for immediate use. It would be interesting to see whether controlling neurons in the decoder would be more effective. Weaknesses: Multiple NMT systems are necessary to discover important neurons. The authors mention that it would be possible to use different checkpoints from a single model, but don*t evaluate how well this would work. The findings in this paper do not lead to immediate translation performance improvements. Questions and other remarks: In Table 4a, why are there 2 results for "-0.25, -0.125, 0"? In section 4.3 (Tense), it may be worthwhile to mention that the neuron is highly activated on the word "Spreads", even if it acts as a noun in this specific sentence. Bottom of p. 6: "Our supervised methods" -> "Our unsupervised methods" To control properties, could SVCCA directions or coefficients be manipulated? Some parentheses around citations are missing or misplaced.