You will be shown sections from a scientific research paper, together with a question about the paper. Paragraphs in the paper are separated by newlines. Your task is to answer the question based on the contents of the paper.

Paper:
----------------------------------------
What does Attention in Neural Machine Translation Pay Attention to?

Attention in neural machine translation provides the possibility to encode relevant parts of the source sentence at each translation step. As a result, attention is considered to be an alignment model as well. However, there is no work that specifically studies attention and provides analysis of what is being learned by attention models. Thus, the question still remains that how attention is similar or different from the traditional alignment. In this paper, we provide detailed analysis of attention and compare it to traditional alignment. We answer the question of whether attention is only capable of modelling translational equivalent or it captures more information. We show that attention is different from alignment in some cases and is capturing useful information other than alignments.

Introduction
Neural machine translation (NMT) has gained a lot of attention recently due to its substantial improvements in machine translation quality achieving state-of-the-art performance for several languages BIBREF0 , BIBREF1 , BIBREF2 . The core architecture of neural machine translation models is based on the general encoder-decoder approach BIBREF3 . Neural machine translation is an end-to-end approach that learns to encode source sentences into distributed representations and decode these representations into sentences in the target language. Among the different neural MT models, attentional NMT BIBREF4 , BIBREF5 has become popular due to its capability to use the most relevant parts of the source sentence at each translation step. This capability also makes the attentional model superior in translating longer sentences BIBREF4 , BIBREF5 .
Figure FIGREF1 shows an example of how attention uses the most relevant source words to generate a target word at each step of the translation. In this paper we focus on studying the relevance of the attended parts, especially cases where attention is `smeared out' over multiple source words where their relevance is not entirely obvious, see, e.g., “would" and “like" in Figure FIGREF1 . Here, we ask whether these are due to errors of the attention mechanism or are a desired behavior of the model.
Since the introduction of attention models in neural machine translation BIBREF4 various modifications have been proposed BIBREF5 , BIBREF6 , BIBREF7 . However, to the best of our knowledge there is no study that provides an analysis of what kind of phenomena is being captured by attention. There are some works that have looked to attention as being similar to traditional word alignment BIBREF8 , BIBREF6 , BIBREF7 , BIBREF9 . Some of these approaches also experimented with training the attention model using traditional alignments BIBREF8 , BIBREF7 , BIBREF9 . liu-EtAl:2016:COLING have shown that attention could be seen as a reordering model as well as an alignment model.
In this paper, we focus on investigating the differences between attention and alignment and what is being captured by the attention mechanism in general. The questions that we are aiming to answer include: Is the attention model only capable of modelling alignment? And how similar is attention to alignment in different syntactic phenomena?
Our analysis shows that attention models traditional alignment in some cases more closely while it captures information beyond alignment in others. For instance, attention agrees with traditional alignments to a high degree in the case of nouns. However, it captures other information rather than only the translational equivalent in the case of verbs.
This paper makes the following contributions: 1) We provide a detailed comparison of attention in NMT and word alignment. 2) We show that while different attention mechanisms can lead to different degrees of compliance with respect to word alignments, global compliance is not always helpful for word prediction. 3) We show that attention follows different patterns depending on the type of the word being generated. 4) We demonstrate that attention does not always comply with alignment. We provide evidence showing that the difference between attention and alignment is due to attention model capability to attend the context words influencing the current word translation.

Related Work
liu-EtAl:2016:COLING investigate how training the attention model in a supervised manner can benefit machine translation quality. To this end they use traditional alignments obtained by running automatic alignment tools (GIZA++ BIBREF10 and fast_align BIBREF11 ) on the training data and feed it as ground truth to the attention network. They report some improvements in translation quality arguing that the attention model has learned to better align source and target words. The approach of training attention using traditional alignments has also been proposed by others BIBREF9 , BIBREF8 . chen2016guided show that guided attention with traditional alignment helps in the domain of e-commerce data which includes lots of out of vocabulary (OOV) product names and placeholders, but not much in the other domains. alkhouli-EtAl:2016:WMT have separated the alignment model and translation model, reasoning that this avoids propagation of errors from one model to the other as well as providing more flexibility in the model types and training of the models. They use a feed-forward neural network as their alignment model that learns to model jumps in the source side using HMM/IBM alignments obtained by using GIZA++.
shi-padhi-knight:2016:EMNLP2016 show that various kinds of syntactic information are being learned and encoded in the output hidden states of the encoder. The neural system for their experimental analysis is not an attentional model and they argue that attention does not have any impact for learning syntactic information. However, performing the same analysis for morphological information, belinkov2017neural show that attention has also some effect on the information that the encoder of neural machine translation system encodes in its output hidden states. As part of their analysis they show that a neural machine translation system that has an attention model can learn the POS tags of the source side more efficiently than a system without attention.
Recently, koehn2017six carried out a brief analysis of how much attention and alignment match in different languages by measuring the probability mass that attention gives to alignments obtained from an automatic alignment tool. They also report differences based on the most attended words.
The mixed results reported by chen2016guided, alkhouli-EtAl:2016:WMT, liu-EtAl:2016:COLING on optimizing attention with respect to alignments motivates a more thorough analysis of attention models in NMT.

Comparing Attention with Alignment
As mentioned above, it is a commonly held assumption that attention corresponds to word alignments. To verify this, we investigate whether higher consistency between attention and alignment leads to better translations.

Measuring Attention-Alignment Accuracy
In order to compare attentions of multiple systems as well as to measure the difference between attention and word alignment, we convert the hard word alignments into soft ones and use cross entropy between attention and soft alignment as a loss function. For this purpose, we use manual alignments provided by RWTH German-English dataset as the hard alignments. The statistics of the data are given in Table TABREF8 . We convert the hard alignments to soft alignments using Equation EQREF10 . For unaligned words, we first assume that they have been aligned to all the words in the source side and then do the conversion. DISPLAYFORM0 
Here INLINEFORM0 is the set of source words aligned to target word INLINEFORM1 and INLINEFORM2 is the number of source words in the set.
After conversion of the hard alignments to soft ones, we compute the attention loss as follows: DISPLAYFORM0 
Here INLINEFORM0 is the source sentence and INLINEFORM1 is the weight of the alignment link between source word INLINEFORM2 and the target word (see Equation EQREF10 ). INLINEFORM3 is the attention weight INLINEFORM4 (see Equation EQREF4 ) of the source word INLINEFORM5 , when generating the target word INLINEFORM6 .
In our analysis, we also look into the relation between translation quality and the quality of the attention with respect to the alignments. For measuring the quality of attention, we use the attention loss defined in Equation EQREF11 . As a measure of translation quality, we choose the loss between the output of our NMT system and the reference translation at each translation step, which we call word prediction loss. The word prediction loss for word INLINEFORM0 is logarithm of the probability given in Equation EQREF12 . DISPLAYFORM0 
Here INLINEFORM0 is the source sentence, INLINEFORM1 is target word at time step INLINEFORM2 , INLINEFORM3 is the target history given by the reference translation and INLINEFORM4 is given by Equation EQREF2 for either non-recurrent or input-feeding attention models.
Spearman's rank correlation is used to compute the correlation between attention loss and word prediction loss: DISPLAYFORM0 
where INLINEFORM0 and INLINEFORM1 are the ranks of the attention losses and word prediction losses, respectively, INLINEFORM2 is the covariance between two input variables, and INLINEFORM3 and INLINEFORM4 are the standard deviations of INLINEFORM5 and INLINEFORM6 .
If there is a close relationship between word prediction quality and consistency of attention versus alignment, then there should be high correlation between word prediction loss and attention loss. Figure FIGREF13 shows an example with different levels of consistency between attention and word alignments. For the target words “will" and “come" the attention is not focused on the manually aligned word but distributed between the aligned word and other words. The focus of this paper is examining cases where attention does not follow alignment, answering the questions whether those cases represent errors or desirable behavior of the attention model.

Measuring Attention Concentration
As another informative variable in our analysis, we look into the attention concentration. While most word alignments only involve one or a few words, attention can be distributed more freely. We measure the concentration of attention by computing the entropy of the attention distribution: DISPLAYFORM0 

Attention Concentration
In word alignment, most target words are aligned to one source word. The average number of source words aligned to nouns and verbs is 1.1 and 1.2 respectively. To investigate to what extent this also holds for attention we measure the attention concentration by computing the entropy of the attention distribution, see Equation EQREF16 .
Figure FIGREF28 shows the average entropy of attention based on POS tags. As shown, nouns have one of the lowest entropies meaning that on average the attention for nouns tends to be concentrated. This also explains the closeness of the attention to alignments for nouns. In addition, the correlation between attention entropy and attention loss in case of nouns is high as shown in Figure FIGREF28 . This means that attention entropy can be used as a measure of closeness of attention to alignment in the case of nouns.
The higher attention entropy for verbs, in Figure FIGREF28 , shows that the attention is more distributed compared to nouns. The low correlation between attention entropy and word prediction loss (see Figure FIGREF32 ) shows that attention concentration is not required when translating into verbs. This also confirms that the correct translation of verbs requires the systems to pay attention to different parts of the source sentence.
Another interesting observation here is the low correlation for pronouns (PRON) and particles (PRT), see Figure FIGREF32 . As can be seen in Figure FIGREF28 , these tags have more distributed attention comparing to nouns, for example. This could either mean that the attention model does not know where to focus or it deliberately pays attention to multiple, somehow relevant, places to be able to produce a better translation. The latter is supported by the relatively low word prediction losses, shown in the Figure FIGREF22 .

Attention Distribution
To further understand under which conditions attention is paid to words other than the aligned words, we study the distribution of attention over the source words. First, we measure how much attention is paid to the aligned words for each POS tag, on average. To this end, we compute the percentage of the probability mass that the attention model has assigned to aligned words for each POS tag, see Table TABREF35 .
One can notice that less than half of the attention is paid to alignment points for most of the POS tags. To examine how the rest of attention in each case has been distributed over the source sentence we measure the attention distribution over dependency roles in the source side. We first parse the source side of RWTH data using the ParZu parser BIBREF16 . Then we compute how the attention probability mass given to the words other than the alignment points, is distributed over dependency roles. Table TABREF33 gives the most attended roles for each POS tag. Here, we focus on POS tags discussed earlier. One can see that the most attended roles when translating to nouns include adjectives and determiners and in the case of translating to verbs, it includes auxiliary verbs, adverbs (including negation), subjects, and objects.

Conclusion
In this paper, we have studied attention in neural machine translation and provided an analysis of the relation between attention and word alignment. We have shown that attention agrees with traditional alignment to a certain extent. However, this differs substantially by attention mechanism and the type of the word being generated. We have shown that attention has different patterns based on the POS tag of the target word. The concentrated pattern of attention and the relatively high correlations for nouns show that training the attention with explicit alignment labels is useful for generating nouns. However, this is not the case for verbs, since the large portion of attention being paid to words other than alignment points, is already capturing other relevant information. Training attention with alignments in this case will force the attention model to forget these useful information. This explains the mixed results reported when guiding attention to comply with alignments BIBREF9 , BIBREF7 , BIBREF8 .

Table 2: Performance of our experimental system in BLEU on different standard WMT test sets.

Table 6: List of the universal POS tags used in our analysis.

Figure 4: Correlation between word prediction loss and attention loss for the input-feeding model.

Figure 5: Attention entropy and its correlation with attention loss for the input-feeding system.

Figure 6: Correlation of attention entropy and word prediction loss for the input-feeding system.

Table 8: Distribution of attention probability mass (in %) over alignment points and the rest of the words for each POS tag.
----------------------------------------

Question: What useful information does attention capture?

To answer the question, format your response as a `json` object with two fields:

"answer": A string providing a succinct answer to the question, in your own words.
"evidence": An array of strings. Each entry should be a full paragraph from the paper. Together, the evidence should serve as a justification for the answer.

For instance, for the question "What baselines did the authors compare against?", a sample response might be:

{
  "answer": "BERT and RoBERTa."
  "evidence": ["We compare our approach against two baselines. In Table 1, we compare against BERT. In Table 2, we compare against RoBERTa. Our findings indicate that our approach improves over both baeslines..."]
}

The "answer" field should be roughly 33 characters in length.

Do not include any text in your response other than the json. If the question is unanswerable given the provided excerpts, respond with the single word "null".

To repeat, the question is: What useful information does attention capture?

Answer JSON object:
{"answer": "Alignment points of the POS tags.", "evidence": ["One can notice that less than half of the attention is paid to alignment points for most of the POS tags. To examine how the rest of attention in each case has been distributed over the source sentence we measure the attention distribution over dependency roles in the source side. We first parse the source side of RWTH data using the ParZu parser BIBREF16 . Then we compute how the attention probability mass given to the words other than the alignment points, is distributed over dependency roles. Table TABREF33 gives the most attended roles for each POS tag. Here, we focus on POS tags discussed earlier. One can see that the most attended roles when translating to nouns include adjectives and determiners and in the case of translating to verbs, it includes auxiliary verbs, adverbs (including negation), subjects, and objects."]}