An empirical evaluation of AMR parsing forlegal documents
Vu Trong Sinh and Nguyen Le Minh
Japan Advanced Institute of Science and Technology (JAIST)
sinhvtr@jaist.ac.jp, nguyenml@jaist.ac.jp

Abstract. Many approaches have been proposed to tackle the problem of Abstract Meaning Representation (AMR) parsing, helps solving
various natural language processing issues recently. In our paper, we
provide an overview of different methods in AMR parsing and their performances when analyzing legal documents. We conduct experiments ofdifferent AMR parsers on our annotated dataset extracted from the English version of Japanese Civil Code. Our results show the limitations
as well as open a room for improvements of current parsing techniques
when applying in this complicated domain.
Keywords: abstract meaning representation · semantic parsing · legal
text.

1

Introduction

In Natural Language Processing, semantic representation of text plays an important role and receives growing attention in the past few years. Many semantic
schemes have been proposed, such as Groningen Meaning Bank [1], Abstract
Meaning Representation [13], Universal Conceptual Cognitive Annotation [17].
In which, Abstract Meaning Representation (AMR) has shown a great potential
and gained popularity in computational linguistics [25] [8] [10] [12].
AMR is a semantic representation language that encodes the meaning of asentence as a rooted, directed, edge-labeled, leaf-labeled graph while abstracting
away the surface forms in a sentence. Every vertex and edge of the graph are labeled according to the sense of the words in a sentence. AMR can be represented
in PENMAN notation, for a human to read and write easily, or graph structure,
for a computer to store in its memory, or decomposed into conjunctions of logicaltriples, for calculating the difference among AMRs. Table 1 shows an example of
AMR annotation for the sentence ”The boy wants to go” with different formats
mentioned above.
AMR has been applied as an intermediate meaning representation for solving
various problems in NLP including machine translation [11], summarization [15],event extraction [27], [21], [14], machine comprehension [20]. For AMR to be
useful in these problems, the AMR parsing task, which aims to map a natural
language string to an AMR graph, plays a crucial role. Despite the advantages
in handling semantic attributes of text, there are not many works exploring the

2

Vu Trong Sinh and Nguyen Le Minh

Table 1: Abstract meaning representation for the sentence ”The boy wants togo” in three formats∃w, b, g : instance(w, want − 01)(w / want-01∧ instance(g, go − 01) ∧:arg0 (b /boy)instance(b, boy):arg1(g / go-01)∧ arg0(w, b) ∧ arg1(w, g) ∧:arg0 b)arg0(g, b)

want-01
:ARG0

:ARG1
:ARG0

boy

go-01

application of AMR in analyzing legal documents. Unlike other domains, understanding legal text faces a number of challenges due to the special characteristics
such as complicated structures, long sentences, domain-specific terminology.
In this paper, we would like to investigate the potential of AMR in thisinteresting field. We provide an overview of main approaches in current AMR
parsing techniques in section 2. From each approach, we choose best systems
that already published the source code to conduct our experiments. In section
3, we revise the dataset JCivilCode-1.0 introduced in 2017 by Lai et al. [22]with some modifications and additional samples. We also extract sentences withvarious lengths from a well-known dataset LDC2017T10 1 in common domain
to have more observation on the performances of each system. Our results andsome discussions are provided in section 4.

2

Approaches in AMR parsing

2.1

AMR notation

In AMRs, each node is named by an ID (variable). It contains the semantic
concept, which can be a word (e.g. boy) or a PropBank frameset (e.g. want01 ) or a special keyword. The keywords consist of entity type (e.g. date-entity,ordinal-entity, percentage-entity), quantities (e.g. distance-quantity), and logicalconjunction(e.g. and, or ). The edge between two vertices is labeled using morethan 100 relations including frameset argument index (e.g. :ARG0, :ARG1 ),semantic relations (e.g. :location, :name), relations for quantities (e.g. :quant,:unit, :scale), relations for date-entities, relations for listing (e.g. :op1, :op2,:op3 ). AMR also provides the inverse form of all relations by concatenating -of
to the original relation (e.g. :location vs :location-of ). Hence, if r is a directed
relation of two entities a and b, we have R(a, b) ≡ R − of (b, a). This inverse
relation helps keep the focus on the entity instead of the verb sense as default.
The task of parsing a natural language text into an AMR graph faces a lotof challenges, such as word-sense disambiguation, semantic graph construction,
data sparsity. Many approaches have been proposed to tackle this problem. They
can be divided into three main categories: alignment-based, grammar-based andmachine-translation-based.
1

https://catalog.ldc.upenn.edu/ldc2017t10

An empirical evaluation of AMR parsing for legal documents

w1

w2

w3

w4

3

w5

relation(a) Alignment-based method(b) Grammar-based method
graph concept

w1

w2

w3

source sentence

c1

c2







c2

c3

graph concept(c) Machine-translation-based method

Fig. 1: Main approaches in AMR parsing

2.2

Alignment-based parsing

One of the pioneer AMR parsing solutions is JAMR introduced by Flanigan
et al. in 2014 [7], which build a two-part algorithm that first identifies concepts
with an automatic aligner and then identifies the relations that it obtains between these by searching for the maximum spanning connected subgraph from anedge-labeled, directed graph representing all possible relations between the identified concepts. This method provided a strong baseline in AMR parsing. Follow
this approach, Zhou et al. [28] extended the relation identification tasks witha component-wise beam search algorithm. Chunchuan and Titov [5] improved
this method by considering alignments as latent variables in a joint probabilistic
model. They used variational autoencoding technique to perform the alignment
inference and archieved the state-of-the-art in AMR parsing until now. But thesource code for this model has not been published completely yet. In this paper,
we take the JAMR model [7] to analyze and conduct experiments.
The core idea of alignment-based methods is to construct a concept set byaligning the Propbank concepts with the words that evoke them. The authorsbuild an automatic aligner that uses a set of rules to greedily align concepts
to words. The authors use WordNet to generate candidate lemmas and a fuzzy
match of a concept, defined to be a word in the sentence that has the longest
string prefix match with that concept’s label. For instance, the fuzzy match for
apply-01 could be aligned with ”application” if this word is the best match in4

Vu Trong Sinh and Nguyen Le Minhthe sentence. Figure 2 shows an example of aligning words in a sentence with
AMR concepts. This JAMR aligner is widely used in many later works.

Fig. 2: Alignment of the words span ”New York City” with AMR fragment [7]In the first stage of identifying concepts, given a sentence w = {w1 , w2 , ..., wn },the parser segments w into subsequences, denoted {wb0 :b1 , wb1 :b2 , ..., wbk−1 :bk },called contiguous spans. A span {wbi−1 :bi } is then assigned to a concept graphfragment ci from the concept set clex{wbi−1 :bi }, or to θ for words that evoke noconcept. This assigning between sequence of spans b and concept graph fragment
c is calculated by a score function:
score(b, c; θ) =kX

θT f (wbi−1 :bi , bi−1 , ci ),

(1)

i=1

where f is a feature vector representation of a span and one of its concept graphfragments in the sentence. The features can be fragment given words, length ofthe matching span, name entity recognizing or bias.
To find the highest-scoring between b and c, JAMR uses a semi-Markov
model. Let S(i) be the score of the first i words of the sentence (wo:i ). Then S(i)is calculated recurrently via the previous scores, with the initialization S(0) = 0.
Obviously, S(n) becomes the best score. To obtain the best scoring concept
labeling, JAMR uses back-pointers method, similar to the implementation of
the Viterbi algorithm [18].
The second stage is to identify the relation, which sets the edge label among
the concept subgraph fragments assigned in the previous stage. The authors
tackle this stage like a graph-based dependency parser problem. While the dependency parser aims to find the maximum-scoring tree over words from thesentence, the relation identifier tries to find the maximum-scoring among subgraphs that preserve concept fragments from the previous stage.
To train the two stage parser, the authors formulate the training data forconcept identification and relation identification separately. In both tasks, theinput must be annotated with name entities (obtained from Illinois Name Entity
Tagger), part-of-speech tags and basic dependencies (obtained from Stanford
Parser). The detail settings and hyper-parameters can be found in the original
paper. This parser has been evaluated the first time on LDC2013E117 corpus (inAn empirical evaluation of AMR parsing for legal documents

52014), archived the Smatch score of 0.58 , and the second time on LDC2015E86
corpus (in 2016), which showed great improvement with 0.67 Smatch score.
2.3

Grammar-based parsing

After the success of Flanigan et al. with an alignment-based approach [7], Wanget al. [24] introduced a grammar-based (or transition-based) parser called CAMR.
The authors first use a dependency parser to generate the dependency tree forthe sentence, then transform the dependency tree to an AMR graph throughsome transition rules. This method takes advantages of the achievements in dependency parsing, with a training set much larger than the training set of AMR
parsing. Damonte et al. [16]. Brandt et al. [2], Goodman et al. [9] and Peng etal. [26] also applied the grammar-based algorithm in their works and obtainedcompetitive results. Figure 3 shows an example of the dependency tree and the
AMR graph parsed from the same sentence ”Private rights must conform to the
public welfare”.

conformnsubj
rightsaux

conform-01
nmod

must

welfareamod
Private

:ARG1

case
to

det
the

right
amod
public:ARG1-ofprivate-02:ARG2-ofobligate-01

:ARG1

:ARG2
welfare:ARG1-ofpublic-02

Fig. 3: Dependency tree and AMR graph generated from the sentence ”Privaterights must conform to the public welfare”

Unlike the dependency tree of a sentence, where each word corresponds toa node in the tree, in AMR graph, some words become abstract concepts or
relations while other words are simply deleted because they do not contributeto the meaning. This difference causes many difficulties for aligning word tokens
and the concept. In order to learn the transition from the dependency tree to
AMR graph, Wang et al. [24] use the algorithm from JAMR to produce thealignment. The authors also construct a span graph to represent an AMR graphthat is aligned with the word tokens in a sentence. This span graph is a directed,
labeled graph G = (V, A), where V = {si,j |i, j ∈ (0, n) and j > i } is a set ofnodes, and A ⊆ V × V is a set of arcs. Each node si,j in G is assigned a concept
label from concept set LV and is mapped with a continuous span (wi , ..., wj−1 )in the sentence w. Each arc is also assigned a relation label from relation set LA .
Basically, CAMR will perform three types of actions to transform the dependency tree into the AMR graph: actions performed when an edge is visited,actions performed when a node is visited, and actions used to infer abstract
concepts in AMR that does not correspond to any word or word sequence inthe sentence. For the details of these actions, readers can refer to the original
paper [23], the Boosting version [4] and the paper at Semeval2016 contest [24].

6

Vu Trong Sinh and Nguyen Le Minh

A disadvantage of this method is that it limits the parsing ability to a single
sentence, because the dependency tree can cover only the structure inside asentence.
Damonte et al. [16] developed a transition-based model called AMREager
that also parses the AMR graph based on transition rules, but differs fromCAMR which requires the full dependency tree to be obtained and then process
the tree bottom-up, this parser process the sentence left-to-right. AMREager
defines a stack, a buffer and a configuration to perform the transition actions,which can be: Shift, LArc, RArc or Reduce. AMREager also uses the alignment
obtained from JAMR aligner to map indices from the sentence to AMR graphfragments. Although the result in Smatch score is still lower than CAMR and
JAMR by a small margin, AMREager obtains best results on several subtasks
such as Name Ent. and Negation.
2.4

Machine-translation-based parsing

Recently, with the achievement of the encoder-decoder architecture in deep neural networks, several supervised learning approaches have been proposed in orderto deal with AMR parsing task. They attempt to linearize the AMR in Penman
notation to sequences of text, at character-level [19] or at word-level [12] [8], sothat the parsing task can be considered as a translation task, which transforms
a sentence into an AMR-like sequence. In this paper, we choose NeuralAMR(word-level linearization) [12] and Ch-AMR (character-level linearization) [19]to run our experiments.

Table 2: AMR linearization for the phrase ”US officials” in NeuralAMR - theleft side is the original AMR and the right side is the linearized string(o / obligate-01:ARG1 (r / right-05(obligate-01:ARG1(right-05:ARG1-of (p / private-02)):ARG1-of (private-02)) :ARG2:ARG2 (c / conform-01(conform-01 :ARG1 (right-05):ARG1 r
:ARG2 (welfare :ARG1-of (public:ARG2 (w / welfare02)))):ARG1-of (p2 / public-02))))Given an AMR graph represented in Penman notation, NeuralAMR preprocesses the graph through a series of steps: AMR linearization, anonymization,and other modifications which aim to reduce the complexity of the linearized
sequences and to address sparsity from certain open class vocabulary entries,
such as named entities and quantities. Representing AMR graphs in this way,
NeuralAMR takes advantage of sequence-to-sequence model by using a stackbidirectional LSTM encoder to encode the input sequence and a stacked LSTM

An empirical evaluation of AMR parsing for legal documents

7

to decode from the hidden states produced by the encoder. The output stringof the model is converted back to AMR format to complete the parsing process.
Since this approach requires a huge amount of labeled data, NeuralAMR uses
paired training procedure to bootstrap a high-quality AMR parser from millionsof unlabeled Gigaword sentences. With this extra dataset, the parsing result increases significantly, from 0.55 to 0.62 in Smatch score. However, it is difficult
for this linearization method to keep the structure of the original graph. In theexample shown in Figure 1(b), the distance between two nodes: ”obligate-01”
and ”conform-01”, which are directly connected in the graph, becomes 5 (tokens) in the linearized string, as shown in Table 2. This distance can be evenlarger in long sentences with complicated structure, thus causes many mistakes
in the annotation.

Fig. 4: Preprocessing data in Ch-AMR - The sentence is converted to sequenceof characters with POS tag in uppercase following each word, the AMR graphis linearized and removed all variables

Different from Kontas et al. [12], Noord and Bos [19] introduce another approach in linearizing which transforms the AMR graph to the character-level.
This model removes all variables from the AMRs and duplicate co-referring
nodes. The input sentences are also tokenized in character-level, along with thepart-of-speech tag of the original tokens to provide more linguistic information
to the decoder. An example of such a preprocessed AMR is shown in Figure 4
Obviously, this preprocessing method causes losing information, since the variables cannot be put back perfectly. To tackle this limitation, the authors describe
an approach to restore the co-referring nodes in the output. All wikification relations present in AMRs in the training set are also removed and restored ina post-processing step. This model archives a better result, with 0.71 Smatchscore.

3

Dataset and evaluation

3.1

DatasetThe original dataset used for testing in this paper is JCivilCode-1.0, which is
introduced by Lai et al. in [22]. In our work, we revised JCivilCode-1.0 carefully
with some modifications and extracted more 48 articles to complete the first four8

Vu Trong Sinh and Nguyen Le Minh

chapter in Part I of the Japanese Civil Code. All the AMRs are annotated by agroup of annotators to ensure the neutrality of evaluation. Table 3 shows somestatistics of this dataset after our revision.
As we mentioned in section 1, one of the main difficulty in analyzing legal
documents is dealing with long sentences. In our experiments, we also would liketo assess the performances of the five models with different length of the sentence.
Since the current legal dataset is still small, we use extra sentences extracted fromthe well-known LDC2017T10 dataset, which consists of nearly 40,000 sentencesin news domain. We divide the test set of LDC2017T10 into four subsets LDC-20,
LDC-20-30, LDC-30-40, LDC-40 with the lengths of the sentences in range 0-20,
21-30, 31-40 and greater than 40 words, respectively. We excluded the samples
containing sub-sentences inside (annotated ”multi-sentence” by the annotators).
This exclusion guaranteed a fair comparison among the five parsers because
CAMR is unable to analyze multiple sentences at the same time.

Table 3: JCivilCode-1.0 statistic
128
Number of samples
Average length31
Max sentence length
107
Average number of graph nodes 28
Max number of graph nodes
96
Vocabulary size
796
Number of tokens
4042

3.2

Evaluation

AMR parsers are evaluated mainly by Smatch score [3]. Given the parsed graphs
and the gold graphs in the form of Penman annotations, Smatch first tries tofind the best alignments between the variable names for each pair of graphs
and it then computes precision, recall and F1 of the concepts and relations. In
this paper, to test the performance of AMR parser on legal text, which contains
sentences in complicated structures, we analyze the parsing results in a deeper
measurement. Specifically, we use the test-suite introduced by Damonte et al.
[16], which assesses the parsing results on various sub-score as follow:– Unlabeled : Smatch score computed on the predicted graphs after removing
all edge labels (e.g., :ARG0, :condition)– No WSD: Smatch score while ignoring Propbank senses (e.g., perform-01 vs
perform-02 )– Name Entity: F-score on the named entity recognition (:name roles)– Wikification: F-score on the wikification (:wiki roles)– Negation: F-score on the negation detection (:polarity roles)

An empirical evaluation of AMR parsing for legal documents

9– Concepts: F-score on the concept identification task
– Reentrancies: Smatch computed on reentrant edges only
– SRL: Smatch computed on :ARG-i roles only
In our experiment with JCivilcode-1.0, we do not include the Wikification andName Entity criteria since there are no Wiki concepts included in this dataset,and the number of existing named entities is small.

4

Experiments and discussion

To evaluate the performance of different parsing strategies on legal text, we
conduct experiments on five models that already provided their source codes:
JAMR, CAMR, AMR-Eager, NeuralAMR and Ch-AMR. While JAMR, CAMR
and AMR-Eager were trained with the LDC2015E86 dataset only (the older
version of LDC2017T10), NeuralAMR and Ch-AMR initialized the parser by
LDC2015E86 and then used an extra corpus of 2 millions sentences extracted
from a free text corpus Gigaword [6] to train the complete models. We provide
some statistics about LDC2015E86 and LDC2017T10 in Table 4. English sentences in these two datasets are collected from TV program transcriptions, webblogs and forums. Each sample in these datasets includes a pair of sentence and
AMR graph corresponding.

Table 4: LDC2015E86 and LDC2017T10 number of samples
Total Train Dev Test
DatasetLDC2015E86 19,572 16,833 1,368 1,371
LDC2017T10 39,260 36,521 1,368 1,371
LDC-20694
LDC-20-30
284
LDC-30-40
143
LDC-4082

Parsing results are summarized in Table 5. The first row is the Smatch scoreof five models on LDC2015E86 dataset, reported in their original papers. The
four next rows are the Smatch score on LDC2017T10 test set with different
ranges of sentence length and the remaining rows show the Smatch score andsub-scores on JCivilCode-1.0.
Overall, the Smatch score of all the parsers on JCivilCode-1.0 is still lower
than on LDC2015E86 by a large margin. It can be figured out that grammarbased and alignment-based methods showed promising results over MT-based
method. JAMR and CAMR archieved the best score on LDC2017T10 long sentences and JCivilCode-1.0 dataset, respectively, while AMREager’s performancewas competitive on both tasks.
In LDC2017T10 experiments, JAMR remained the best parser in every rangeof sentence length. The gap between this method and the others even becomes

10

Vu Trong Sinh and Nguyen Le Minh

Table 5: Smatch scores and sub-scores of five models
LDC2015E86
LDC-20
LDC-20-30
LDC-30-40
LDC-40JCivilCode-1.0
Unlabeled
No WSD
Negation
Concepts
Reentrancies
SRL

JAMR CAMR AMREager NeuralAMR Ch-AMR
0.67
0.66
0.64
0.62
0.71
0.71
0.66
0.69
0.65
0.45
0.68
0.62
0.64
0.59
0.43
0.66
0.60
0.62
0.56
0.42
0.65
0.59
0.62
0.54
0.40
0.45
0.48
0.43
0.39
0.28
0.50
0.56
0.53
0.46
0.37
0.47
0.50
0.45
0.40
0.28
0.23
0.16
0.32
0.35
0.19
0.59
0.63
0.62
0.52
0.35
0.32
0.35
0.31
0.29
0.22
0.43
0.47
0.41
0.00
0.28

larger when parsing longer sentences. Although grammar-based methods focus
on constructing the structure of the graph based on its corresponding dependency
tree, CAMR and AMREager are unable to provide better output than JAMR.
In legal text parsing experiments, CAMR outperforms the others on both the
Smatch score and many sub-scores. Specifically, this method obtains best resultsin constructing graph topology (Unlabeled ), predicting the Propbank sense (NoWSD and SRL) as well as identifying concepts in AMR graphs (Concepts score).
When parsing graphs containing cycles, CAMR also performs better, as shown
in Reentrancies scores.
We analyze some common errors in parsing outputs of legal sentences, withthe statistic represented in Figure 5 and the examples provided in Table 6. Oneof the most common error in alignment-based and grammar-based performancesis missing concept and relation related to modal verbs. In legal documents, modal
verbs (e.g. ”may”, ”can”, ”must”) play a crucial role in a sentence and decide
whether an action is permitted or not. This differs from other domains, wherethese words do not often contribute a lot to the sentence meaning. As shownin example 1 in Table 6, only NeuralAMR is capable of identifying the concept”obligate-01” while other models totally ignore it.
Another challenge in parsing legal text is the logical complexity. In this aspect, all the parsers still show limitation when parsing negative clause. This is
not too surprising as many negations are encoded with morphology (e.g., suchas un- prefix in ”unless” or ”unable”) and cause difficulties for detection. We
show an example in Table 6 the output from all the parsers for a sentence: ”No
abuse of rights is permitted”. NeuralAMR and JAMR succeeded in converting
negation to :polarity -, AMREager didn’t put this edge to the exact position,
but in this case, it doesn’t change the meaning of the sentence. CAMR evenperforms worse as it skips this important information.

An empirical evaluation of AMR parsing for legal documents

11

Table 6: Common errors types: Incorrect concept - Incorrect relation Missing concept - Missing attribute
ExamplePrivate rights must conform to the No abuse of rights is permitted (2)public welfare (1)Gold annota- (o / obligate-01(p / permit-01
tion:ARG1 (r / right-05:polarity :ARG1-of (p / private-02)):ARG1 (a / abuse-01:ARG2 (c / conform-01:ARG1 (r / right-05))):ARG1 r
:ARG2 (w / welfare:ARG1-of (p2 / public-02))))
JAMR(c / conform-01(p / permit-01:ARG1 (r / right:ARG1 (a / abuse-01:ARG1-of (p2 / private-03)):ARG1 (r / right)):ARG2 (w / welfare:polarity -):domain-of (p / public)))

CAMR

AMR-Eager(missing concept obligate-01)(x2 / right-05:ARG1-of (x1 / private-03):ARG1-of (x4 / conform-01:ARG2 (x8 / welfare:mod (x7 / public))))(x6 / permit-01:ARG1 (x2 / abuse-01:ARG1 (x4 / right)))(missing-)(missing concept obligate-01)(v3 / conform-01:ARG1 (v2 / right:ARG1-of (v1 / private-03)):ARG2 (v5 / welfare:mod (v4 / public)))(missing concept obligate-01)
Neural-AMR (o / obligate-01:arg2 (r / rule-out-02:arg0 (r2 / right:arg1-of (p / private-03)):arg1 (w / welfare:mod (p2 / public))))Ch-AMR(vv1conform-01 / conform-01:ARG1 (vv1person / person:ARG1-of (vv1private-03
private-03)):ARG2 (vv1welfare / welfare:ARG1-of vv1))

attribute:polarity(v3 / permit-01:ARG1 (v1 / abuse-01
polarity :ARG1 (v2 / right)))(p / permit-01:polarity :arg1 (a / abuse-02:arg1 (r / right)))(vv3permit-01 / permit-01:ARG1 (vv3no-abuse / no/ abuse))(missing
concept”right-05”,”public-02”, ”obligate-01” )(missing concept ”right-05”)

12

Vu Trong Sinh and Nguyen Le Minh
1800

1600

1400

Number of errors

1200

1000

800

600

400

200

0
Incorrect relation

Incorrect concept
JAMR

CAMR

AMREager

Missing concept

Missing attribute

NeuralAMR

Fig. 5: Common error types

5

Conclusions

We conducted experiments of AMR parsing on the legal dataset JCivilCode1.0 and news domain dataset LDC2017T10 with different ranges of sentence
length to observe the abilities of five different models. The parsing outputs were
evaluated by Smatch metric in several aspects including overall F-score and subscore on specific tasks. Experimental results showed the domain adaptation offive models for the legal domain and the performance decreased of approximately
0.2 on the Smatch score. This result shows difficulties in applying AMR parsing
for analyzing legal documents.
Currently, our legal dataset JCivilcode is still too small comparing to LDC2017T10.
In order to improve the domain adaptation ability for current approaches as wellas to obtain a fully evaluation, the legal dataset has to be enlarged. This workrequires a lot of efforts from experts in both linguistic and legal domain.

Acknowledgment
This work was supported by JST CREST Grant Number JPMJCR1513, Japan.

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