Comparing RNN and Transformer Context Representations in the Czech

Answer Selection Task

Marek Medved’, Radoslav Sabol and Aleš Horák

Natural Language Processing Centre, Faculty of Informatics, Masaryk University,

Botanická 68a, 602 00, Brno, Czech Republic

Keywords:

Question Answering, Answer Context, Answer Selection, Czech, Sentece Embeddings, RNN, BERT.

Abstract:

Open domain question answering now inevitably builds upon advanced neural models processing large un-

structured textual sources serving as a kind of underlying knowledge base. In case of non-mainstream highly-

inﬂected languages, the state-of-the-art approaches lack large training datasets emphasizing the need for other

improvement techniques. In this paper, we present detailed evaluation of a new technique employing various

context representations in the answer selection task where the best answer sentence from a candidate docu-

ment is identiﬁed as the most relevant to the human entered question. The input data here consists not only

of each sentence in isolation but also of its preceding sentence(s) as the context. We compare seven different

context representations including direct recurrent network (RNN) embeddings and several BERT-model based

sentence embedding vectors. All experiments are evaluated with a new version 3.1 of the Czech question

answering benchmark dataset SQAD with possible multiple correct answers as a new feature. The comparison

shows that the BERT-based sentence embeddings are able to offer the best context representations reaching

the mean average precision results of 83.39% which is a new best score for this dataset.

1 INTRODUCTION

The main strategy of open domain question answer-

ing (QA) systems which exploit a large underly-

ing unstructured textual knowledge base usually fol-

lows the same core schema. First, the documents

which are most related to the answer are identiﬁed

(document selection), then the “central point” (e.g.

a sentence) containing or supporting the answer is

searched for (answer selection), and ﬁnally the spe-

ciﬁc exact answer is extracted or deduced from the

selected sentence (answer extraction). Such split ap-

proach is not always followed with big models e.g.

ALBERT (Lan et al., 2019) or ELECTRA (Clark

et al., 2020) ﬁne-tuned on large QA datasets such as

SQuAD (Rajpurkar et al., 2016), RACE (Lai et al.,

2017) or GLUE (Warstadt et al., 2019). But for

smaller datasets with non-mainstream languages this

approach allows to ﬁne tune the individual modules

separately.

In this paper, we present the results of a new en-

hancement in the answer selection task for highly in-

ﬂected languages. The proposed method is described

and evaluated within the QA system AQA (Medved’

and Horák, 2016; Medved’ and Horák, 2018) and

the Czech question answering dataset SQAD (Sabol

et al., 2019) consisting of more than 13,000 QA pairs.

The main idea of the new technique lies in employ-

ing various types of answer context representations

to supplement the information contained in candidate

answer sentences with directly preceding communi-

cation. For the purpose of context speciﬁcation, two

main approaches are evaluated and compared – word

vector representations with recurrent neural networks

and sentence vector representations with the trans-

former models.

Section 2 details the answer selection task within

the Czech SQAD database and describes the core neu-

ral network architecture used in the answer selection

module. The next section presents the seven context

representations for the two model types that were im-

plemented and evaluated. And the last section offers

the results and discussion for each of the context types

with selected output examples. The best representa-

tion was based on a Czech BERT model named Cz-

ert (Sido et al., 2021) reaching 83.39% of mean av-

erage precision as a new best result with the SQAD

dataset.

388

Medved, M., Sabol, R. and Horák, A.

Comparing RNN and Transformer Context Representations in the Czech Answer Selection Task.

DOI: 10.5220/0010827000003116

In Proceedings of the 14th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2022) - Volume 3, pages 388-394

ISBN: 978-989-758-547-0; ISSN: 2184-433X

BiGRU

Hidden Question

Sequence

Attention

Layer

Context

Mechanism

Answer Sequence

(word embeddings)

Hidden Answer

Sequence

Final

Question

Representation

Final

Answer

Representation

Cosine

Similarity

Question Sequence

(word embeddings)

Figure 1: The overall Answer Selection module architecture.

2 THE ANSWER SELECTION

TASK

The Simple Question Answering Database, or

SQAD (Medved’ et al., 2020), is a benchmark dataset

designed for evaluating various Question Answering

(QA) tasks for the Czech language. The latest ver-

sion SQAD 3.1 consists of 13,473 question-answer

records harvested with an underlying knowledge base

of 6,571 different Czech Wikipedia articles. Each

record contains several metadata ﬁles. The Question

ﬁle stores the question text created by a human anno-

tator. The Text ﬁle contains the full Wikipedia arti-

cle. The Answer Selection ﬁle presents the sentence

containing the exact answer (the shortest phrase fully

answering the question), which is instantiated in the

Answer Extraction ﬁle. The remaining metadata ﬁles

describe the category (type) of the question and the

answer.

In the ﬁnal SQAD database, all records are pre-

processed and enriched with automatic PoS-tagging

and word embedding information. To achieve speed

and space efﬁciency, the ZODB

Python library is

employed to encode the database information with-

out duplicities. The Vocabulary table stores all

words with their base forms, part of speech tags,

and the corresponding embedding vectors in differ-

ent dimensions. FastText (Bojanowski et al., 2017)

vectors are prepared with the dimensions of 100,

300 and 500 and BERT (pre-trained Slavic Bert

model (Arkhipov et al., 2019a)) vectors with the size

of 768. The Knowledge Base table contains all the

involved Wikipedia articles where all words are rep-

resented by word IDs leading to the chosen vector

representation. Moreover, each sentence is stored in

several sentence vector embedding forms such as pre-

https://zodb.org/en/latest/

computed Sentence-BERT (Reimers and Gurevych,

2019) vectors or the CLS token vector provided by

the Hugging Face BERT

transformer, both loading

the Slavic BERT model.

2.1 The Neural Model and the Learning

Process

The neural network architecture of the answer selec-

tion module (see Figure 1) is based on the Siamese

network schema as introduced by Santo et al (San-

tos et al., 2016). The network input is thus formed

by a pair (q, a), where q and a are matrices of word

embeddings for the question and a candidate answer

respectively.

The ﬁrst layer is deﬁned as a bi-directional Gated

Recurrent Unit (BiGRU) with shared weights for both

inputs q and a. The output of this layer forms matri-

ces Q (question) and A (answer). The following layer

uses an attention mechanism computing the matrix

G = Q

WA, where W is an attention matrix of learn-

able parameters. Row-wise and column-wise max

pooling is used to create the ﬁnal representations of

q and a that are compared using the cosine similarity

measure as the ﬁnal score of the input pair.

In terms of learning, the objective to minimize is

deﬁned as the Pairwise Ranking Loss,

loss(p

, p

−

) = max{0, m − p

+ p

−

}

where p

and p

−

represent positive and negative ex-

amples, and m is a constant margin (0

1 is used in this

paper). For performance and memory reasons, the al-

gorithm samples 50 negative examples along with the

single correct example. As there are multiple nega-

tive examples during the training procedure, the one

https://huggingface.co/transformers/model_doc/bert.

html

Comparing RNN and Transformer Context Representations in the Czech Answer Selection Task

389

Transformer ContextRNN Context

Context Sequence

(word embeddings)

BiGRU

Hidden Question

Sequence

Answer Sequence

(word embeddings)

Hidden Answer

Sequence

Question Sequence

(word embeddings)

Hidden Context Sequence

concatenation

Context

Sequence

(sentence

embeddings)

BiGRU

Hidden Question

Sequence

Answer Sequence

(word embeddings)

Hidden Answer

Sequence

Question Sequence

(word embeddings)

Hidden Context

Sequence

concatenation

BiGRU

Figure 2: The speciﬁcation of Answer Selecion context for both RNN and transformer-based approaches.

which produces the highest loss is used for the back-

propagation.

The training algorithm iterates 25 times over the

training data, and the model with the best validation

accuracy is selected for the ﬁnal evaluation. The for-

ward pass and backpropagation is computed on a sin-

gle NVIDIA Tesla T4 GPU with 16GB VRAM, 320

Turing Tensor Cores, and 2560 CUDA cores avail-

able.

3 ANSWER CONTEXT

The key idea behind the exploitation of answer con-

text in Question Answering is to ﬁnd out if some fea-

tures/knowledge obtained from answer context can

help the ﬁnal model to identify the correct answer

more precisely. Very often the question directly spec-

iﬁes several important clues that navigate the answer

selection process through the text. However, this clue

information is often distributed not only in the ﬁnal

answer sentence but also in the preceding text.

For example, Figure 3 contains the phrase "band

Apocalyptica" as the main clue of the question while

the main focus of the question is "number of band

members". Here the answer sentence does not even

mention the "band Apocalyptica" clue. Without the

previous context sentence, even a human reader is not

able to conﬁrm whether the answer sentence provides

the required information about the Apocalyptica band

or about some other band that can be mentioned in the

article.

The ﬁnal system therefore needs to beneﬁt from

the information represented by the context sentences

if the database connects the ﬁnal answer to its context.

3.1 Context Types in SQAD 3.1

The new SQAD v3.1 database is extended with sev-

eral new context representations in two types. The

ﬁrst type offers a direct extension of the candidate an-

swer sentence serving the context as input to the same

recurrent network (RNN) layer as used for the answer.

The second type uses various pre-trained transformer

representations of the whole preceding sentence (or

sentences) as the context. Each of these types needs

a different inclusion technique into the module neural

architecture as detailed in the following section.

In case of the RNN context type, three context

variants have been evaluated. The most straightfor-

ward approach uses the full preceding sentence(s),

word by word, as the context. The second vari-

ant extracts only noun phrases, as the most probable

bearer of a clue information. In this case, the noun

phrases are identiﬁed with the SET parser (Ková

et al., 2011). The third variant again extracts pos-

sible information clues which are now speciﬁed as

so called link named entities (Medved’ et al., 2020).

The entities are searched in the text using a separate

BERT-NER model trained to mark all phrases used as

internal links in the Czech Wikipedia.

3.2 Encoding the Context

The answer selection module as speciﬁed in Sec-

tion 2.1 can incorporate different context mechanisms.

The RNN contexts come in the form of individual

word vectors that represents the context sentences or

ICAART 2022 - 14th International Conference on Agents and Artiﬁcial Intelligence

390

Table 1: The best hyperparameter values for various context types.

Context Type BiGRU Hidden Size Learning Rate Dropout

SENT_1 RNN context 380 0.0004 0.4

PHR_2 RNN context 380 0.0002 0.4

NER_5 RNN context 320 0.0006 0.4

SENT_1 Transformer ctx 480 0.0007 0.2

Question:

Kolik ˇclen˚u má v souˇcasnosti ﬁnská hudební skupina Apocalyptica?

[How many members does the Finnish band Apocalyptica currently have?]

Correct answer:

Skupina je složena ze tˇrí (p˚uvodnˇe ˇctyˇr) klasických violoncellist˚u.

[The band is composed of three (originally four) classical cellists.]

Answer context (previous sentence):

Apocalyptica je ﬁnská hudební skupina, jejíž zvláštností je interpretace p˚uvodnˇe heavy metalových skladeb

osobitým zp˚usobem aranžovaných pro violoncello.

[Apocalyptica is a Finnish band whose peculiarity is the interpretation of originally heavy metal compositions

arranged in a special way for cello.]

Figure 3: Example of answer context.

phrases. The transformer contexts are in the form of

sentence vectors encoding the whole sentences.

Figure 2 details two necessary architecture mod-

iﬁcations for the two main context types. The ﬁrst

modiﬁcation is composed of contexts represented by

a sequence of word embeddings separated by a special

[SEP] (separator) token. The separator introduces

each full sentence, noun phrase of link named entity

selected for the context.

The particular word embeddings are derived from

the same embedding model

as used for the questions

and candidate answers, and the internal sequence rep-

resentation is provided by the same RNN layer. The

resulting context word vectors are then concatenated

to the answer sequence before the attention mecha-

nism.

The second context form is constructed with sen-

tence embeddings derived from a transformer-based

model. Unlike in the ﬁrst group, each sentence is rep-

resented by one vector. As this information is not di-

rectly comparable to the question/answer words, the

internal context representation is thus obtained from

a separate BiGRU layer trained with the sentence em-

beddings input. The new representation is concate-

nated in the same way as in the ﬁrst group. Cur-

rent implementation evaluates four sentence embed-

ding models:

• the CLS vector provided by the Slavic BERT

model (Arkhipov et al., 2019b)

• the CLS vector of the Czert model (Sido et al.,

2021)

The FastText word embedding vectors are used in the

presented experiments.

• the CLS vector of the RobeCzech model (Straka

et al., 2021)

• the Sentence-BERT model representa-

tion (Reimers and Gurevych, 2019)

4 EXPERIMENTS AND

EVALUATION

In the following experiments, all the presented con-

text variants have been evaluated with the SQADv3.1

dataset. The dataset comes divided into three bal-

anced subsets used for training, test and validation

with 8,059, 4,013 and 1,401 records.

4.1 Multiple Correct Answers in SQAD

3.1

Detailed error analyses of question answering tech-

niques with previous SQAD versions revealed a pos-

sible misconception in identifying a single sentence

as the only correct answer selection result. An exam-

ple in Figure 4 demonstrates a case where the exact

answer is present in multiple sentences. Any of these

sentence then serves as a correct input to the ﬁnal an-

swer extraction module. Therefore the current SQAD

version extends the metadata with a list of all sen-

tences containing the exact answer as a possible an-

swer selection result. Table 3 presents overall dataset

statistics of the multiple answer lists where almost a

half of the records contains more than one possible

answer sentence. For comparison purposes, the mod-

Comparing RNN and Transformer Context Representations in the Czech Answer Selection Task

391

Table 2: The answer selection results for different context type models. The best RNN model is in italics, the overall best

model is bold.

Single answers Multiple answers

Context Type MAP MRR MAP MRR

SENT_1 RNN context 81.94 88.03 84.76 90.09

PHR_2 RNN context 82.23 88.20 84.98 90.20

NER_5 RNN context 82.42 88.40 85.14 90.35

SENT_1 with Czert 83.39 89.18 85.79 90.93

SENT_1 with RobeCzech 82.75 88.68 85.29 90.55

SENT_1 with Slavic BERT 83.05 88.88 85.59 90.74

SENT_1 with S_BERT 82.65 88.58 85.14 90.41

Question:

Proˇc dostal Albert Einstein Nobelovu cenu za fyziku?

[Why Albert Einstein receive the Nobel Prize in Physics?]

Selected answer:

Za vysvˇetlení fotoelektrického jevu získal Einstein roku 1921 Nobelovu cenu za fyziku.

[For explaining the photoelectric effect, Einstein won the 1921 Nobel Prize in Physics.]

Correct answer:

V roce 1921 byl Einstein ocenˇen Nobelovou cenou za fyziku za " vysvˇetlení fotoefektu a za zásluhy o teoretickou

fyziku " .

[In 1921, Einstein was awarded the Nobel Prize in Physics for "explaining the photo effect and for his contri-

butions to theoretical physics."]

Figure 4: Answer in two sentences example (record 011880).

ule offers the evaluation results fort both the single

answer and the multiple answer forms.

4.2 Results and Comparison

The following experiments use a ﬁxed context win-

dow depending on the type of context. A single pre-

vious sentence (SENT_1) was used for both the RNN

full sentence context and for the transformer contexts.

The noun phrases RNN context was evaluated with all

noun phrases from preceding two sentences (PHR_2)

and the link named entity context used ﬁve preceding

named entities (NER_5).

The answer selection model was trained using the

RMSprop optimization algorithm, where the learning

rate, dropout, batch size, and the BiGRU hidden size

Table 3: SQAD 3.1 statistics of multiple answer sentences.

Number of sentences displays how many sentences of the

underlying document contain the exact answer. In case of

0 sentences, the exact answer is not directly represented in

the text, e.g. with a yes/no question.

0 sentences 1387 records

1 sentence 5964 records

2 sentences 1751 records

3 sentences 869 records

4 sentences 514 records

5 sentences 383 records

≥ 6 sentences 2605 records

were the optimized hyperparameters. The best hy-

perparameter settings were found using the Optuna

hyperparameter optimization framework, as shown in

Table 1.

Table 2 presents the ﬁnal results of the mean av-

erage precision (MAP) and the mean reciprocal rank

(MRR) using both the single and multiple answer se-

tups. We may see that the multiple answer setup

brings the results in a narrower range eliminating pos-

sible correct exact answers that are evaluated as incor-

rect in the single answer setup. All values are aver-

ages from three runs of the best optimized model.

For a comparison, the most recent published best

result for the SQADv3 test set was 82.91% MAP

in (Medved’ et al., 2020). The same model setup

(RNN SENT_1) achieved 81.94% MAP score in the

current SQADv3.1 version which thus serves as a

baseline here. The best-performing context type was

the transformer context using Czert (Sido et al., 2021)

sentence embeddings, which reached MAP score of

83.39%, a 1.45% improvement over the baseline. For

the new evaluation with multiple correct answers, the

same best model achieved 85.79% of the mean aver-

age precision with less differences between the mod-

els. The NER_5 model is the winner of the RNN

word models, proving the informative value of the

link named entities selected as the clue information.

Figure 5 offers an example where the winning Cz-

ert sentence representation provided more informative

ICAART 2022 - 14th International Conference on Agents and Artiﬁcial Intelligence

392

Question:

Kdy zaˇcala vysílat TV Nova?

[When did the Nova TV start broadcasting?]

Czert model result (correct):

TV Nova je ˇceská komerˇcní televizní stanice.

[Nova TV is a Czech commercial TV station.]

Licenci na vysílání získala v roce 1993, první vysílání prob

ehlo až 4. února 1994 v Praze.

[It obtained a broadcasting license in 1993, the ﬁrst broadcast however started on the 4th of February

1994 in Prague.]

RNN SENT_1 result (incorrect):

Na Novu pˇrichází i nové zahraniˇcní seriály jako Námoˇrní vyšetˇrovací služba, Kriminálka Miami, Kriminálka

New York, Ztraceni, Dr. House a mnoho dalších.

[Nova TV starts broadcasting new foreign series such as NCIS, C.S.I. Miami, C.S.I. New York, Lost, House

M.D. and more.]

Od poloviny

ríjna 2007 za

cala TV Nova vysílat zpravodajství (ranní, odpolední i ve

cerní) v obrazovém

formátu 16:9 a v rozlišení HDTV.

[From the half of October 2007, the Nova TV started to broadcast news (morning, afternoon and

evening) in 16:9 format and in HD resolution.]

Figure 5: An example record (000267) where the Czert transformer model improved the RNN SENT_1 incorrect result. The

answer selection sentence is in bold, while the preceding context is in italics.

context representation allowing to choose the correct

answer sentence.

5 CONCLUSIONS AND FUTURE

DIRECTIONS

We have presented the details and evaluation of a new

technique of employing various forms of preceding

contexts to improve the answer selection task. The

designed model is able to incorporate both word-

based context representations as well as sentence-

based context representations. The effectiveness of

this approach was evaluated with seven variants of

both context types. The best results were achieved us-

ing transformer-based sentence context representation

of the Czech BERT model named Czert. This model

reached 1.45% improvement over the baseline and the

resulting mean average precision of 83.39% is a new

best result for the SQAD benchmark dataset with the

single answer setup and 85.79% with the new multi-

ple correct answer evaluation.

In the future work, we plan to test model setups

with varying context lengths and to decide whether

longer contexts can improve the performance. We

will also plug the new answer selection module into

the complete AQA pipeline for open domain question

answering and evaluate the whole system with the lat-

est version of the SQAD database.

ACKNOWLEDGEMENTS

This work has been partly supported by the Ministry

of Education of CR within the LINDAT-CLARIAH-

CZ project LM2018101.

Access to computing and storage facilities owned

by parties and projects contributing to the National

Grid Infrastructure MetaCentrum provided under the

programme "Projects of Large Research, Develop-

ment, and Innovations Infrastructures" (CESNET

LM2015042), is greatly appreciated.

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