Annotating Zero Anaphora for Question Answering
Yoshihiko Asao, Ryu Iida, Kentaro Torisawa
National Institute of Information and Communications Technology
Kyoto 619-0289, Japan{asao, ryu.iida, torisawa}@nict.go.jp
Abstract
We constructed a large annotated dataset of zero pronouns that correspond to adjuncts marked by -de (translated to English as in, at, byor with) in Japanese. Adjunct zero anaphora resolution plays an important role in extracting information such as location and means froma text. To our knowledge, however, there have been no large-scale dataset covering them. In this paper, focusing on the application ofzero anaphora resolution to question answering (QA), we proposed two annotation schemes. The first scheme was designed to efficiently
collect zero anaphora instances that are useful in QA. Instead of directly annotating zero anaphora, annotators evaluated QA instances
whose correctness hinges on zero anaphora resolution. Over 20,000 instances of zero anaphora were collected with this scheme. We
trained a multi-column convolutional neural network with the annotated data, achieving an average precision of 0.519 in predicting thecorrectness of QA instances of the same type. In the second scheme, zero anaphora is annotated in a more direct manner. A modeltrained with the results of the second annotation scheme performed better than the first scheme in identifying zero anaphora for sentences
randomly sampled from a corpus, suggesting a tradeoff between application-specific and general-purpose annotation schemes.
Keywords: zero anaphora, question answering, convolutional neural networks

1. Introduction
Zero anaphora refers to anaphora in which the anaphor hasa phonetically null form. Zero anaphora resolution is an
important sub-problem of many NLP tasks such as question
answering (QA). To successfully apply machine learningalgorithms to zero anaphora, it is crucial to construct a large
and consistent annotated dataset.
In this paper, we focus on zero pronouns that correspondto non-obligatory adjuncts marked by postposition -de inJapanese, which mark the location, means, reason or manner of an event and can be translated to English prepositions
such as in, at, by or with. Although the identification of adjunct zero pronouns plays an important role in extracting
such information as location and means from a text, to ourknowledge, there have been no attempts to create a largescale dataset covering them.
While it is possible to exhaustively annotate zero anaphora
in a corpus to achieve our goal, such an approach might
be inefficient when we are interested only in instances thatare useful in a specific application. Given this, we propose two different annotation schemes in this paper. In thefirst scheme, annotators annotate QA instances that potentially involve zero anaphora. In the second scheme, annotators directly annotate noun-predicate pairs with regard towhether they are in a zero anaphora relationship. We evaluated the performance of these two schemes using an existing neural network-based zero anaphora resolution method(Iida et al., 2016). Our experimental results show that thefirst scheme achieved better performance when it is used totrain a module for a QA system, suggesting that the effectiveness of an annotation scheme depends on applications
even in a relatively well studied task like zero anaphora resolution. Conversely, the model trained with annotation results of the second scheme achieved better performance in
identifying zero anaphora for sentences randomly sampledfrom a corpus.
We collected 20,830 instances of -de zero anaphora with

レンタルバイクを借りて島をまわる。I rent a motorcyclei
and travel the island [by ∅ ].
i

ソーラー発電が拡大すると環境はどう変化するのか 。 How does our environment change [by ∅i ] if
solar electricityi expands?
名古屋市立大学は、市民と科学者が喫茶店でコーヒーを飲
みながら科学について話し合う「サイエンスカフェ」を名古屋市内で開催する。Nagoya City University will hold a‘science café’ in the city of Nagoyai , in which citizens andscientists discuss science [in ∅ ] while drinking coffee at aicafé.

Table 1: Examples of -de zero anaphora in our data
our first annotation scheme alone, while the Kyoto University Text Corpus (Kawahara et al., 2002), the largest existing resource that we are aware of, has only 333 instancesof -de zero anaphora of the equivalent type. Table 1 showsa few illustrative examples of zero anaphora successfullycollected in our work.1

2. Related work
Anaphora or coreference has been annotated in several projects including Message Understanding Conference (MUC) (Hirschman and Chinchor, 1997), AutomaticContext Extraction (ACE) (Doddington et al., 2004) and
OntoNotes (Hovy et al., 2006), but zero anaphora is notannotated in their English corpora. The OntoNotes corpora for pro-drop languages like Chinese and Arabic contain coreference annotations for certain types of zero pronouns. They do not, however, include adjunct zero pronouns, which we deal with in this paper.
Another kind of resources that are relevant to our work
is annotated corpora of semantic roles or frame elements
such as PropBank (Palmer et al., 2005) and FrameNet

3523

1

We only deal with intra-sentential anaphora in this paper.

(Baker et al., 1998). In FrameNet, for example, frame elements that are not overtly encoded are annotated as Null Instantiation, some of which can be regarded as adjunct zero
anaphors, although their antecedents are not annotated.
As for Japanese resources, zero anaphora was annotatedin 5,000 sentences of the Kyoto University Text Corpus(Kawahara et al., 2002), including -de zero anaphora, although its size is small; it has only 333 instances of
intra-sentential -de zero anaphora. Zero anaphora isalso annotated for 20,000 sentences in the NAIST corpus(Iida et al., 2007), but only for -ga (nominative), -o (accusative) and -ni (dative).
While zero anaphora resolution has been recognized as animportant task in pro-drop languages such as Chinese andJapanese, the task has been less prominent in languages like
English, in which core arguments are usually realized as
overt forms. However, adjuncts can be omitted in any language, and in such cases, they must be inferred from thecontext. For example, consider the following English sentence: Shortly after her arrival in Tokyo, she began her career as a journalist. Given this sentence, we can infer that
she began her career as a journalist in Tokyo, but it is not atrivial task to identify this implicit relation. Thus, although
the target language of our current work is Japanese, it has
relevance to other types of languages as well.

language processing does not directly modify improve, but
we can clearly see that it is the means of the action denotedby the predicate.
Solving -de zero anaphora is useful in a variety of applications including question answering. For example, given
sentence (4), to build a QA system that can correctly answer
to the question ‘What can we improve medicine with?’, thesystem must resolve -de zero anaphora. More generally, -deanaphora resolution plays a crucial role when we would liketo extract information like location and means that must be
inferred from context.2

4.

To generate datasets for annotation, we used WISDOM X(Mizuno et al., 2016), a question-answering system that ourteam has been developing.3 The factoid QA module of
WISDOM X accepts a question in Japanese and returns
nouns as answers, as well as original sentences from the
web corpus that support the answers. An example is below.
(5) Question AI-denani-gajitsugensuru
AI-INSTR what-NOM be.realized‘What will be realized by AI?’
Answer kaji-robotto ‘housekeeping robot’
Sentence AI-ga
sarani hattensureba,
AI-NOM further develop.if
kaji-robotto-ga
jitsugensuru daroo.
housekeeping-robot-NOM be.realized will
‘If AI develops further, housekeeping robots will
be brought into reality.’

3. -de zero anaphora of JapaneseIn this paper, we focus on adjuncts marked by -de inJapanese. The postposition -de in Japanese marks the location, means, reason, or manner of an event or action denoted by the predicate it modifies; it can be translated into
a number of English prepositions including in, at, by andwith depending on the context. Examples of the usage of de are shown below. In sentence (1), -de marks the locationof the action, whereas in sentence (2), -de marks the meansby which the action is performed.
(1) kōen-de(2) supūn-dehashirupark-LOC run‘run in a park’taberuspoon-INSTR eat‘eat with a spoon’In some cases, a -de phrase is not overt, but can be recovered from the context. Such cases constitute examples of-de zero anaphora:

Human annotators judge whether the answer is correct, orin other words, whether the sentence supports the answer
given the question. In this example, we expect that the annotators’ judgment is positive. When the judgment is positive, we can suppose that the sentence entails housekeeping
robots will be realized by AI, although the -de phrase (translated as by AI) is not overtly expressed. This indicates thatthe original sentence involves zero anaphora, as shown insentence (6).
(6) AIi -ga

sarani hattensureba,
AI-NOM further develop.if
kaji-robotto-ga∅dejitsugensuru daroo.
ihousekeeping-robot-NOM
be.realized will
‘If AIi develops further, housekeeping robots will be
brought into reality by ∅i .’(3) Kare-wa gakkōi -ni itte∅debenkyōshitai
he-TOP school-to go.andstudied‘He went to school and studied at ∅ ’iData constructioniThus, this annotation task can be regarded as an indirect
way to discover zero anaphora instances. With this scheme,(4) shizen-gengo-shorii -okatsuyōshite
natural-language-processing-ACC utilize.and∅de iryō-okaizendekiru

2imedicine-ACC improve.can‘We can improve medicine with ∅ utilizing
inatural language processingi ’
In sentence (3), the location of studying is not directly expressed, but can be inferred from the preceding part, hewent to school. In the same vein, in sentence (4), natural

Assuming zero pronouns for adjuncts may be nonconventional, but this is just one way of capturing covert semantic
relationships between nouns and predicates in text. Instead of using the notion of adjunct zero anaphora, we can say that there are
implicit semantic links between ‘school’ and ‘study’ in sentence(3) and between ‘natural language processing’ and ‘improve’ insentence (4) that can be expressed by postposition -de. This alternative view does not affect our discussion.
3
http://wisdom-nict.jp/3524

KyotoKyoto
Kyoto

Binary pattern
instances DB
tourists visit Kyoto
eat ramen in Kyoto
buy souvenir in Kyoto

target noun(potential
antecedent) question
generation

WhateattoXeatin Kyotoin Kyoto

4 billion page
web corpus

Kyoto
eat X
Q
A tofuanswergeneration

If you visit Kyoto, I
recommend you eat
tofu.
sentence

Figure 1: Construction of QA instances
we can focus on zero anaphora instances that are likely to be
useful in QA, rather than zero anaphora in general. Anotheradvantage of this annotation scheme is that annotators do
not have to know what zero anaphora is, and therefore nonexperts can more readily work on it.
In our approach, QA instances for annotation are automatically generated by combining the question suggestion module and the factoid QA module of WISDOM X. Figure 1
illustrates our workflow, which we also summarize below.
1. Choose a -de candidate (e.g., Kyoto)
2. Generate questions:(a) Extract binary pattern instances that contain the-de candidate (e.g., eat ramen in Kyoto)(b) Generate questions based on these binary pattern
instances (e.g., What is there to eat in Kyoto?)
3. Generate answers:(a) Decompose the questions into a -de candidate
and a unary pattern(b) Search for sentences with both the -de candidate
and the unary pattern
In the first step, we choose a noun that is frequently usedwith -de, which we call the -de candidate. The following
steps are designed to find a zero anaphor which has this -decandidate as the antecedent.
In the second step, we generate questions using the question suggestion module. The module works in the following way. First, it searches for binary pattern instances inour database that contain the -de candidate.4 Here a binary pattern instance refers to a dependency tree fragmentthat consists of a predicate and two nouns that depend on
it. For example, when the -de candidate is Kyoto, examples of binary pattern instances include ⟨kankōkyaku-ga
Kyōto-o otozureru⟩ ‘tourists visit Kyoto’, ⟨Kyōto-de rāmeno taberu⟩ ‘eat ramen in Kyoto’ and ⟨Kyōto-de kaigi-ga hirakareru⟩ ‘a conference is held in Kyoto’. While the -decandidate Kyoto may appear in a variety of syntactic positions, we only use questions in which it appears with de. Next, questions are generated by replacing the non-decandidate noun with an interrogative word. For example,
4

Our binary pattern database is based on the four-billion-page
web corpus that we constructed, which is also used by the factoid
QA module.

the question Kyōto-de nani-o taberu (literally ‘eat what in
Kyoto’, i.e., ‘what is there to eat in Kyoto?’) is generated
based on such instances as ⟨Kyōto-de rāmen-o taberu⟩ ‘eatramen in Kyoto’.
In the third step, the generated questions are input into our
QA system, WISDOM X. While WISDOM X uses multiple methods to find answers, for our present work, we only
use answers obtained via the method that we describe here.
WISDOM X decomposes the question into the -de candidate and the unary pattern that corresponds to the question
minus the -de candidate. Here a unary pattern refers to adependency tree fragment that consists of a predicate and aslot for a noun, such as ⟨X-ni iku⟩ ‘go to X’ or ⟨X-ga aku⟩
‘X opens’. In our ‘eat what in Kyoto’ example, the -de candidate is Kyoto and the unary pattern is ‘eat X’. The systemthen searches for sentences that contain both the unary pattern and -de candidate. We will obtain, for example, thefollowing sentence from the corpus: ‘If you visit Kyoto, I
recommend you eat tofu’. Here, the noun tofu fills X of the‘eat X’ pattern; therefore, tofu is presented as an answer
along with the original sentence, which the answer is basedon. As is the case in this example, we only use sentences inwhich the -de candidate (‘Kyoto’ in this example) and thetarget predicate (‘eat’) are not in a dependency relationship.
We expect sentences collected with this method to have ahigher-than-average chance of involving -de zero anaphora.
This is because our procedure guarantees that the -de candidate is a noun that frequently occurs in the -de position.
Note that, because WISDOM X does not search for contexts beyond a single sentence, our target is limited to intrasentential anaphora.
We created two datasets for annotation, QAAnnot and AllNouns, based on the QA instances generated by the procedure above.
QAAnnot Annotators directly evaluate QA instances that
potentially involve zero anaphora. This task can be
simultaneously interpreted as both a QA evaluation
task and a zero anaphora annotation task. For thistask, we obtained 100,000 QA instances in the following manner. First, we generated questions for 10,000
nouns randomly sampled from the nouns that mostfrequently appear in the -de position in the TSUBAKI corpus (Shinzato et al., 2008) of 600 million web
pages. Next, we randomly sampled questions according to the frequency distribution of the predicates;
these questions were then input into WISDOM X until we obtained 100,000 QA instances. Finally, humanannotators judge each QA instance for its correctness.
AllNouns While QAAnnot may be optimized for QA, its
special annotation scheme may have a negative impact
on performance when it is used to train a model for
identifying -de zero anaphora in general. To investigate this, we created the second dataset called AllNouns; for this dataset, we obtained 10,000 QA instances using the same procedure as QAAnnot. Unlike QAAnnot, however, annotators do not see questions or answers, but instead judge whether each nounin the original sentence is in a -de anaphora relationship with the target predicate. On average there were3525

#sentences #predicates#pairs #-de zero anaphora
QAAnnot
100,000
100,000 100,000 20,830(20.8%)
AllNouns
10,00010,00070,971
7,749(10.9%)
General
1,433
3,790
17,392
1,126(6.5%)
KTC
5,127
14,98790,731333(0.4%)
KTC: Kyoto University Text Corpus (Kawaraha et al. 2002)#annotators
24
8
4
–

approx. person days
500
100
20
–

Fleiss’ κ
0.556
0.539
0.495
–

Table 2: Annotation results

6. Experiments

seven nouns to annotate per sentence. More than one
noun can be simultaneously in a -de anaphora relation
with the same target predicate.
We also created a small third dataset for evaluation, which
we refer to as General. For this dataset, sentences wererandomly sampled from the four-billion-page web corpus.
For each noun-predicate pair that was identified as not
being in a dependency relationship, annotators annotatedwhether it was in a -de anaphora relationship or not. In order to restrict our data to Japanese body texts, we only used
sentences that (i) have at least two postpositions and (ii) end
with the Japanese full stop (。).
To identify nouns and predicates to annotate,
we used the morphological analyzer MeCab(Kudo et al., 2004), as well as the dependency parser
J.DepP (Yoshinaga and Kitsuregawa, 2009).

5. Annotation results
In this section, we describe annotation results obtained
from the annotation tasks described above. Table 2 summarizes the sizes of our datasets as well as our annotation
results.
For each dataset, three annotators independently evaluateeach instance, and the final judgment was determined bya majority vote. An annotator was sometimes replaced byanother person after completing a set of 1,000 instances.
The total numbers of annotators participated in our work, aswell as the time required to create each dataset, are includedin Table 2.
While 20.8% of the instances were judged to be positive
in QAAnnot, the percentage was only 10.9% in AllNouns
and 6.5% in General. This difference is expected given
the way each dataset was built; QAAnnot has the highest
proportion of positive instances because it is designed toannotate only likely candidates of zero anaphora. AllNouns
has a smaller proportion of positive instances because not
only the original -de candidates but also the other nouns
are annotated. The third dataset, General, has the lowest
proportion of positive instances, as it consists of random
sentences in the corpus.
Table 2 also shows a comparison with the Kyoto University
Text Corpus (Kawahara et al., 2002). In the Kyoto University Text Corpus, the number of noun-predicate pairs thatare targets of zero anaphora annotation based on our criteria is 90,731. Among them, only 333 pairs (0.3%) areannotated as -de zero anaphora. The number of -de zero
anaphora that we collected is substantially larger, in termsof both absolute numbers and the frequency relative to thesize of the corpus.

train
62,52744,60344,603-

QAAnnot
QAAnnot-Small
AllNouns
Generalvalid
20,805
14,753
14,753-devel
10,401
7,231
-

test
10,4097,253
18,265

Table 3: Dataset sizes for our experiments
To see how our annotation results could be generalized
to new data, we employed the multi-column convolutional
neural network (MCNN) (Cireşan et al., 2012). An MCNN
is a variant of a convolutional neural network and has multiple independent columns, each of which has its convolutional and pooling layers. MCNNs have recently been successfully used to model subject zero anaphora in Japanese(Iida et al., 2016). In this work, Iida et al. extracted elevendistinct column inputs from the target predicate (pred), the-de candidate (cand) and their context. The column inputs consist of (a) the word sequence of cand and pred, (b)the surface word sequences before cand, between cand andpred and after pred, (c) the four word sequences extracted
from the dependency tree, and (d) pred and the word sequences before and after pred. We used the same elevencolumn inputs for our experiments; our architecture is illustrated in Figure 2. More details on the definition of eachcolumn are given in (Iida et al., 2016).
softmax

cand

pool
conv

pool
conv

pool
conv

pool
conv

pool
conv

pool
conv

pool
conv

pool
conv

pool
conv

pool
conv

col 1

col 2

col 3

col 4

col 5

col 6

col 7

col 8

col 9

col 10 col 11

pred

cand
(-de candidate)
cand

pool
conv

pred

pred
parse

(predicate)predmoshi Kyōto-ni iku-nara, tōfu-o taberu beki.
if
Kyoto-to go-if
tofu-ACC eat
must'You must eat tofu if you visit Kyoto.'

Figure 2: Our multi-column convolutional network architecture
Our MCNN was implemented with Theano(Bastien et al., 2012). We used 300-dimensional wordembedding vectors pre-trained with Wikipedia articles
using Skip-gram with a negative-sampling algorithm(Mikolov et al., 2013). We treated all the words that only
appeared once as unknown words and assigned them arandom vector. We used an SGD with mini-batches of3526

test data
QAAnnot

AllNouns

Generaltraining data
QAAnnot
QAAnnot-Small
AllNouns
QAAnnot
QAAnnot-Small
AllNouns
QAAnnot
QAAnnot-Small
AllNouns

R
0.263
0.199
0.335
0.198
0.124
0.288
0.125
0.081
0.186

P
0.629
0.677
0.523
0.563
0.589
0.569
0.379
0.414
0.413

F
0.371
0.307
0.408
0.293
0.204
0.382
0.188
0.135
0.257

avg. P
0.519
0.510
0.452
0.402
0.369
0.451
0.218
0.197
0.269

Table 4: Performance comparison for different combinations of training and test data
100 and a learning rate decay of 0.95. We used 3-, 4- and5-grams with 100 filters each. Average precision was usedas our evaluation metric. Tuning embeddings in training
was turned off, as we found no performance improvements.
QAAnnot and AllNouns were divided into training, validation, development and test data as summarized in Table
3, such that noun-predicate pairs from the same sentence
were included in the same bin.5 For General, all instanceswere used as test data. Further, because QAAnnot is larger
than AllNouns, we could not easily determine whether the
performance differences between these two datasets were
due to differences in annotation methods or size. To avoid
this complication, we constructed QAAnnot-Small by randomly sampling QAAnnot such that the sizes of training
and validation data exactly matched those of AllNouns;
note that QAAnnot-Small was only used for training.
Table 4 summarizes our results. Regardless of the typeof training data, the average precision was highest when
the test data was QAAnnot, and lowest when the test data
was General. This is probably because QAAnnot has thelargest proportion of positive instances, whereas General
has the smallest.
The model trained with QAAnnot outperformed that of
AllNouns when the test data was QAAnnot, whereas themodel trained with AllNouns outperformed that of QAAnnot when it was AllNouns or General, in terms of average
precision. As expected, the model trained with QAAnnotSmall performed worse than that of QAAnnot for all test
data, but not to the extent that the gap between the twoannotation schemes was reversed, showing that the performance differences between QAAnnot and AllNouns were
not caused by their size differences. Our results suggest that
QAAnnot has an advantage when our goal is to improve a
module of a QA system, while AllNouns is better for identifying -de zero anaphora in randomly sampled sentences.
Figure 3, 4 and 5 show the precision-recall curves corresponding to different training data when QAAnnot, AllNouns and General were used as test data respectively. To
improve QA, we would be able to set a threshold such that
only relatively reliable answers are returned.

Figure 3: The precision-recall curves for experiments inwhich QAAnnot was used as test data

Figure 4: The precision-recall curves for experiments inwhich AllNouns was used as test data

5

The total number of instances does not exactly match the total
number of annotated noun-predicate pairs because sometimes thesame noun occurs more than once in a sentence. In these cases,
annotations were made only once without distinguishing different
occurrences of the same noun, but each occurrence of the noun
was used as a different instance for experiments.

Figure 5: The precision-recall curves for experiments inwhich General was used as test data3527

To the best of our knowledge, this work is the first attempt to identify -de zero anaphora on a large scale, andthus a direct performance comparison with existing work is
not possible. For reference, Ouchi et al. (2017) report zero
anaphora resolution experiments for -ga (nominative), -o(accusative) and -ni (dative); their best F-measures were50.65%, 35.07% and 9.83% respectively. Our best Fmeasure on General is 25.7%, which surpass the best score
for dative, but not for nominative or accusative. This suggests that -de zero anaphora is more difficult to solve than-ga and -o zero anaphora.

7. Conclusions
In this paper, we described our work in constructing a large
annotated dataset of zero pronouns that correspond to adjuncts marked by -de in Japanese. The contrast between
QAAnnot and AllNouns can be equated to the contrast
between an end-to-end approach and a component-based
approach. While QAAnnot performs better for QA, it
does not generalize well to sentences randomly sampledfrom a corpus, suggesting that there is a tradeoff between
application-specific and general-purpose annotation methods.

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