On the Word Boundaries of Emergent Languages Based on Harris's Articulation Scheme
Authors: Ryo Ueda
Taiga Ishii
Yusuke Miyao
Venue: ICLR 2023 poster (2023)
Keywords: Emergent Communication
Emergent Language
Unsupervised Word Segmentation
Harris's Articulation Scheme
Compositionality

Abstract: This paper shows that emergent languages in signaling games lack meaningful word boundaries in terms of Harris's Articulation Scheme (HAS), a universal property of natural language. Emergent Languages are artificial communication protocols arising among agents. However, it is not obvious whether such a simulated language would have the same properties as natural language. In this paper, we test if they satisfy HAS. HAS states that word boundaries can be obtained solely from phonemes in natural language. We adopt HAS-based word segmentation and verify whether emergent languages have meaningful word segments. The experiment suggested they do not have, although they meet some preconditions for HAS. We discovered a gap between emergent and natural languages to be bridged, indicating that the standard signaling game satisfies prerequisites but is still missing some necessary ingredients.

Published as a conference paper at ICLR 2023
ON THE WORD BOUNDARIES OF EMERGENT LAN- GUAGES BASED ON HARRIS'S ARTICULATION SCHEME
Ryo Ueda, Taiga Ishii & Yusuke Miyao
The University of Tokyo (zyoryoueda, raigarana, hut.://ss.uuueeeel...oooooooooor ip
ABSTRACT
This paper shows that emergent languages in signaling games lack meaningful word boundaries in terms of Harris's Articulation Scheme (HAS), a universal property of natural language. Emergent Languages are artificial communication protocols arising among agents. However, it is not obvious whether such a simu- lated language would have the same properties as natural language. In this paper, we test if they satisfy HAS. HAS states that word boundaries can be obtained solely from phonemes in natural language.
adopt HAS-based word segmen- tation and verify whether emergent languages have meaningful word segments. The experiment suggested they do not have, although they meet some precondi- tions for HAS. We discovered a gap between emergent and natural languages be bridged, indicating that the standard signaling game satisfies prerequisites but is still missing some necessary ingredients.
INTRODUCTION
Communication protocols emerging among artificial agents in simulated environment are called emergent languages (Lazaridou & Baroni 2020). It is important to investigate their structure to recognize and bridge the gap between natural and emergent languages, as several structural gaps have been reported (Kottur al. 2017] Chaabouni et al., 2019). For instance, Kottur al. (2017) indicated that emergent languages are not necessarily compositional. Such gaps are undesirable be- cause major motivations in this area are to develop interactive Al (Foerster al.) 2016 Mordatch & Abbeel| 2018 Lazaridou et al. (0200) and to simulate the evolution of human language (Kirby) 2001 Graesser et al. [2019] Dagan et al.) 2021). Previous work examined whether emergent lan- guages have the same properties natural languages, such grammar (van der Wal et al. 2020 entropy minimization (Kharitonov et al. 2020), compositionality (Kottur et al. (017). and Zipf's Word segmentation would be another direc- tion to understand the structure of emergent languages because natural languages not only have construction from word to sentence but also from phoneme word (Martinet) 1960). However, previous studies have not gone so far as to address word segmentation, as they treat each symbol in emergent messages as if it were "word" (Kottur et al. 2017, van der Wal et al., 2020), or ensure that a whole message constructs just one "word" (Chaabouni et al. 2019 Kharitonov et al. 2020).
law of abbreviation (ZLA) (Chaabouni et al. 2019 [
The purpose of this paper to study whether Harris's articulation scheme (HAS) HHaris 1955
Tanaka- Ishil 2021) also holds in emergent languages. HAS is a statistical universal in natural lan- guages Its basic idea is that we can obtain word segments from the statistical information of phonemes but without referring to word meanings. HAS holds not only for phonemes but also for other symbol units like characters. HAS can be used for unsupervised word segmentation [Tanaka- Ishii, 2005) to allow us to study the structure of emergent languages. It should be appropriate to apply such unsupervised methods since word segments and meanings are not available beforchand in emergent languages.
'ZLA states that the more frequently word is used, the shorter it tends to be Zipf 1935
Note that this is different from the famous distributional hypothesis Harris 1954

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Sender
Input /
ENC
DEC
Output R(m)
Input /
Message !!!
ENC
DEC
Cross Entropy
Receiver R
Figure I: Illustration of signaling game. SSection 3.1 gives its formal definition. In each play, sender agent obtains an input and converts it to a sequential message. receiver agent receives the message and converts it to an output. Each agent is represented as an encoder-decode model.
In addition to the absence of ground-truth data on segmentation, it is not even evident, in the first place, whether emergent languages have meaningful word segments. In other words, the prob- lem is whether they have meaningful segments. If not, then it means that find another gap between emergent and natural languages. In this paper, we pose several verifiable questions to an- whether their segments meaningful. Importantly, some of the questions applicable to any word segmentation scheme as well as HAS, so that they can be general criteria for future work on the segmentation of emergent languages.
To simulate the emergence of language, we adopt Lewis's signaling game (Lewis) 1969, This game involves two agents called sender S and receiver Rł and allows only one-way communication I and converts i into sequential message m S(i) E M. Then, R receives m M and predicts the original input. The goal of the game is the correct prediction (mm) i. Figure illustrates the signaling game. Here, we consider the set {m MM m S(i)her as the dataset of an emergent language, to which the HAS- based boundary detection Manaka-Ishil 2005) is applicable. The algorithm yields the segments of messages.
from S to R. In each play, S obtains an imput
Our experimental results showed that emergent languages arising from signaling games satisfy two preconditions for HAS: (i) the conditional entropy (Eg. 2} decreases monotonically, and (ii) the branching entropy (Eq. 1) repeatedly falls and rises. However, it also suggested that the HAS- based boundaries are not necessarily meaningful. Segments divided by the boundaries may not serve meaning units, while words in natural languages do (Martiner) 1960). It is left for future work to bridge the gap between emergent and natural languages in terms of HAS, by giving rise to meaningful word boundaries.
HARRIS'S ARTICULATION SCHEME
This section introduces Harris's articulation scheme (HAS) and the HAS-based boundary detection algorithm. HAS has advantages such being simple, deterministic, and easy-te tooyytt-itterreet while being linguistically motivated. Such simplicity is important particularly when we have neither any prior knowledge ground-truth data of target languages, e.g., emergent languages.
In the paper "From phoneme to morpheme" (Harris) 1955). Harris hypothesized that word bound- aries tend to occur at points where the number of possible successive phonemes reaches local peak in context, Harris (1955) exemplifies the utterance "He's clever" that has the phoneme sequence /hiyzclevar/ The number of possible successors after the first phoneme /h/ is 9: /w.yyi.e.æe.aa. Next, the number of possible successors after /hi/ increases to 14. Likewise, the number of possible phonemes increases to 29 after /hiy/, stays at 29 after /hiyz/, decreases 11 after /hiyzk/, decreases to after /hiyzk//, and so on. Peak numbers are found at /yl, /z/, and /r/, which divides the phoneme sequence into /hiy/+///+//klevrrr. Thus, the utterance is divided into "He". "s", and "clever". Harris's hypothesis can be reformulated from an ieformationnthhooreee of successors with entropy. We review the mathematical formulation of the hypothesis as Harris's articulation scheme (HAS) and the HAS-based boundary detection (Tanaka-Ishi 2005). HAS does involve statistical information of phonemes but does not involve word meanings. This is important because it gives a natural explanation for well-known linguistic concept called double articulation (Martinet 1960). Martinet] 1960) pointed out that languages have two structures: phonemes (irrel- evant meanings) and meaning units (i.e., words and morphemes). HAS can construct meaning units without referring to meanings.
point of view by replacing the number
Thhere may be other representations for the phonemes, but we follow Harris's notation.
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2.1 MATHEMATICAL FORMULATION OF HARRIS'S S HYPOTHESIS
While Harris (9555) focuses on phonemes for word boundary detection, Tanaka-Ishii (2021) sug-
gests that the hypothesis is also applicable to units other than phonemes. Therefore, in this section, set of units is called an alphabel X' as a purely mathematical notion that is not restricted to phonemes.
Tanaka-Ishii (2005) uses characters for the same purpose. Moreover, |Fantzii & Ananiadou (1996)
and Tanaka-Isia Ishii & Ishii (2007) investigate the detection of collocation from words.
Let X' be an alphabet and X be the set of all n-grams on X. We denote by X; random variable of X' indexed by i, and by Xa. a random variable sequence from X; to X3. The formulation by (Tanaaa--ssai Ishika (2005) involves two kinds of entropy: branching entropy and conditional entropy (Cover & Thomas 2006 R The branching entropy of random variable Xn after sequence 8
F0 ... En- E x is defined as:
(ss) = H(X,, 10m-1= = =- P
[) - P(x 1s),
ZEN'
where P(x1 s) = P(X, = x Xon-1 s). Intuitively, the branching entropy h(s) means how
many elements can occur after s or the uncertainty of the next element after s. In addition to h(s), the conditional entropy of random variable Xn, after an n-grum sequence Xo.n- is defined as:
H(n) = H(Xa [xm=-1)= = - P(s)
P(x ZEX
Σ
s) log, P(x s),
(2)
SE.t
where P(s) P(Xonn1 s). The conditional entropy H(n) can be regarded as the mean of h(s) over n-gram sequences E X", since (n) = = xEx" P(s)h(s). ((n) is known to decrease monotonically in natural languages (Bell et al.. 1990). Thus, for a partial sequence To:n-1 E X",
( > 11( holds on average, although h(s) repeatedly falls and rises depending specific s. Based on such properties, Harris's 's articulation scheme (HAS) is formulated as 3
If there some partial sequence Fimn-1 X (n > 1) s.t. h(Ton-2) < h(xom-1), 1), then 2,, is at a boundary.
(3)
2.2 BOUNDARY DETECTION ALGORITHM BASED ON HARRIS'S 'S ARTICULATION SCHEME
This section introduces the based bound-
Algorithm 1 Boundary Detection Algorithm
ary detection algorithu (Tnaka Ishii 2005).
1: + 0; w - 1; B
0
Let s = Tg... 1 E X². We denote by Sty its partial sequence I;.... Tj.. Given
2:
while < n do
3: Compute h($0+4m-1) 1)
and a parameter threshold, the boundary detec-
tion algorithm yields boundaries ( It pro-
4:
if > 1 & h(86)4t-1) - 1 (818440022
threshold then
ceeds as shown in Algorithm [ Entropy-
5:
B + B {i+ w/
based methods are advantageous in simplicity
6:
end if
and interpretability. though they are inferior
7:
if i
+ w < n
1 then
to other -tate-of-thheart methods in terms of F | Simplicity and interpretability are im- portant when we analyze emergent languages for which we have no prior knowledge and have no trrth data of word segmenta- tion. Note also that it is not even evident, in the 12: end while
8:
w w +1
9:
else
10:
iti+ 1; w
11: end if
first place, whether they have meaningful word segments.
Since our targets emergent languages, the outputs of the boundary detection algorithm do not necessarily mean articulatory boundaries. Instead, we call them hypothetical boundaries (hypo- boundaries) and refer to the segments split by hypo-boundaries as hypo-segments.
"The term "branching entropy" is from Tssaka-Ishii & Jin 2008), but the definition per se is quite basic. 5 "Although this is called hypothesis in Tanaka-Ishil (2005). Tanaka-Ishi & Jin (0066) and Tanaka-Iship &
Ishii (2007), we refer to it as scheme following the recent publication (lanaka-Ishi) 202T).
FFor practical reasons, the original algorithm involves another parameter maxlen to ensure w < maxlen.
We omit it because the message length in emergent languages is fixed in this paper (see (Scctinn 3).
Tanaka-Ishii & Jin (2008) reported F-score = 83.6% in English and F-score 83.8% in Chinese. They considerably high scores for hatropy-hased settings.
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3 EMERGENT LANGUAGE ARISING FROM SIGNALING GAME
We have to define environments, agent architectures, and optimization methods for language emer- gence simulations. This paper adopts the framework of [Caabouni et al. (2020). We define an environment in Section 3.1| specify the agent architecture and optimization methods in Section 3.2, and also give an explanation of the compositionality of emergent languages in Sectionn 3.3
3.1 SIGNALING GAME
An environment is formulated based Lewis's signaling game (Lewis 1969). signaling game G consists of quadruple (I,,. S, R). where is an input space, M a mexsage space, S : I- M is sender agent, and R .M I is receiver agent. The goal is the correct reconstruction i = R(S(i)) for all i E I. While the input space I and the message space M are fixed, the agents R are trained for the goal. An illustration of a signaling game is shown in Figure Following Chaabouni et al. (2020), we define I as an attribute-value set Disgs (defined below) and M as a set of discrete sequences of fixed length & over a finite alphaber A:
I D
M A {ar O& 01 EJ.
(4)
Attribute- Value Set Let Natt; Tlyal be positive integers called the number of attributes and the number of values. Then, an altribute-value set Diise is the set of ordered tuples defined as follows:
D. Real
=(1 Unar) By E Dyaal
(5)
This is an abstraction of an attribute-value object paradigm (e.g., Kottur et al. 2017) by Chaabouni al. (2020). Intuitively, each index of vector (vi,... Ujs.... Un is an attribute (e.g., color),
while each by is attribute value (e.g., blue, green, red, and purple)
3.2 ARCHITECTURE AND OPTIMIZATION
We follow Chaabouni et al. 2020 as well for the architecture and optimization method, Each agent is represented an incoder-decoddr model (Figure 1): the sender decoder and the receiver encoder are based on single-layer GRUs (Cho et al.] 2014), while the sender encoder and the receiver decoder linear functions. Each element i E Dina is vectorized to be fed into output from the linear Dual
functions. Formally, each (01,..., Un is converted into the Rat Rlal dimeensinll vector which is the concatenation of nattt. one-hot representations of uy. During training, the sender samples deterministic messages probabilistically. At the test time, it samples them greedily to serve function. Similarly, the receiver's output layer, followed by the Softmax, determines aatt categorical distributions over values {I...., vall } during training. At the test time, Nlatt values are greedily sampled. The agents are optimized with the stochastic computation graph (Schulman et al. 2015) that is a combination of REINFORCE (Williams) 1992) and standard backpropagation. The sender is optimized with the former, while the receiver is optimized with the latter.
3.3 COMPOSITIONALITY OF EMERGENT LANGUAGES
An attribute- value set Dnoo by Chaabouni al. (2020) is an extension of an attribute-value setting Ruat
(Kottur et al., 2017) introduced to measure the compositionality of emergent languages. While the concept of compositionality varies from domain domain, researchers in this area typically regard it as the disentanglement of representation learning. KKotur et al.) (2017). for instance, set up an environment where objects have two attributes: color and shape, each of which has several possible values (e.g., blue, red, for color and circle, star, for shape). They assumed that if a language sufficienlly compositional, each message would be a composition of symbols denoting the color value and shape value separately. This concept has been the basis for subsequent studies (Li & Bowling 2019 Andreas 2019 Ren et al. 2020 Chaabouni et al. 2020).
"Atthough the game is extremely simple, it is suitable for avoiding some pitfalls. Lowe et al, (211 indi- cated that agents might not communicate effectively in more complex games than in signaling game. [Bouhh- court& & Baroni (2018) suggested that agents fail to capture conceptual properties when T. set of images.
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Topographic Similarity Topographic Similarity (TopSim) (Brighton & Kirby 2006 Lazaridou et al. 2018) is the de facto compositionality measure in emergent communication literature. Suppose have distance functions dz, d for spaces I, .M, respectively. TopSim is defined as the Spear- man correlation between distances (i, 12) and (S((S(()). S(12) for all é i2 E I s.t. i * i2- This definition reflects an intuition that compositional languages should map similar (resp. dissim- ilar) imputs to similar (resp. dissimilar) messages. Following previous work using attribute-value objects (e.g., Chaabouni et al. 2020), we define dI as the Hamming distance and dM as the edit distance. Because this paper is about message segmentation, we can consider two types of edit dis- tance. One is the "character" edit distance that regards elements a E A as symbols. The other the "word" edit distance that regards hypo-segments as symbols. Let us call the former C-TopSim and the latter W-Toppim.
Entropy
4 PROBLEM DEFINITION
Q1: H decreses
monotonically?
The purpose of this paper is study whether Harris's articulation scheme (HAS) also holds in emergent languages. However, this question is too vague to answer. We first divide into the following:
Q2: h jitters?
Q1. Does the conditional entropy H decrease monotonically?
Q2. Does the branching entropy h repeatedly fall and rise?
Message
Q3. Do hypo-boundaries represent meaningful boundaries?
Q3 the same as the original question, except that Q3 is slightly more formal. However, we have to answer [Q] and Q2 beforehand, because HAS implicitly takes it for granted that H decreases mono-
Hypo-segments
tonically and h jitters. Although both Q and Q2 generally hold in natural languages, neither is trivial in emergent languages. Figure 2
Q3: Are They Meaningful? Figure Illustration of ques- tions.
illustrates Q Q2 and Q3
straightforward to answer and Q2 as we just need to calculate H and h. In contrast, Q3
is still vague answer, since we do not have prior knowledge about the boundaries of emergent languages and do not even know if they have such boundaries. To mitigate it, we posit the following
necessary conditions for Q3 Let G be game (Diats Ah, S, R). If the answer to Q3 is yes, then:
Cl. the mean number of hypo-boundaries per message should increase as Natt increases,
C2. the size of the vocabulary (set of all hypo-segments) should increase as flaal increases,
C3. W-TopSim should be higher than CoTSSmm.
About C1 and C2 An attribute-value set nes was originally introduced to measure composi- tionality. Composiiionality, in this context, means how symbols in a message separately denote the components of meaning. In case, each segment, or word, can be thought of certain unit that denotes the attribute values, so that the number of words in a message should increase as the corresponding attributes increase. Therefore, if the answer Q3 is yes, then CT should be valid. Likewise, the vocabulary size should be larger in proportion to the number of values Deaal, motivat- ing C2 Here, we mean by vocabulary the set of all hypo-segments. Note that the message length is fixed, because otherwise, the number of hypo-segments would be subject to variable message length as well as (Natt: and the implication of results would be obscure.
C3 comes from the analogy of the linguistic concept called double articulation Mar- In natural languages, meanings are quite arbitrarily related to the phonemes that con- struct them. In contrast, the meanings are less arbitrarily related to the words. The phonemes do not denote meaning units, but the words do. In our case, for example, the attribute-value ob- ject (RED, CIRCLE) seems less compositionally related to the character sequence "r...d,c..rr..Ieee,,, while it seems more compositionally related to the word sequence "red,circle." " This intuition moti- vates
About C3
tinet 1960)
Based on conditions C and is restated as follows: (Q3-1) Does the mean number of hypo-houndaries per message increase Matt increases? (Q3-2) Does the vocabulary size increase
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rvval increases? (Q3-3) Is W- W-TopSim higher than C-TopSim? Importantly, Q3-1
Q3-2 and Q3-
3 are applicable to any word segmentation scheme as well as HAS, as they neither contain Eq. Eq. anymore. Thus, they be general criteria for future work the segmentation of
emergent languages. We also verified that the above conditions CT CT]C2 and C33 are indeed satisfied by synthetic languages that are ideally compositional (see Appendixxx
EXPERIMENTAL SETUP
5.1 PARAMETER SETTINGS
Input Space Ratt and reval have to be varied to answer Q3-1
Q3-2 and Q3-3 while the sizes of
the input spaces [I] = (Nwal) (Tyat)"an Pars must be equal each other to balance the complexities of games. Therefore, we fix [Z] 4096 and vary (Matt: Reaa) ) as follows:
(natt. nyal) E {11, 4096), (2, 64), (3, 6), (4,8), (6, (12,2))
(6)
Message Space The message length and alphabet have be determined for a message space
M Ah We set k = 32, similarly to previous work on ZLA (Chaabouni et al. 2019 Rita et al.
2020 Ueda & Washio 2021) that regards each a E A as a "character." Note that k 32 is set much
longer than those of previous work on compesitionality (Chaabouni et al.) 2020 Ren et al. 2020
Li Bowling 2019) that typically adopts = natt as if each symbol a E A were a "word." We
set A [1, 8J. Its size |A] should be small as possible to avoid the data sparsity issue for boundary detection, and to ensure that each symbol AA serves as a "character." In preliminary experiments, tested |A| E (2, 4, 8, 16) and found that learning is stable when |AA 8.
Architecture and Optimization We follow Chaabouni et al. (2020) for agent architectures, an objective function, and an optimization method. The hidden size of GRU (Cho al., 2014) set to 500, following(hhaabouni Chaabouni et al. (2020). All data from an input space I = Diaat are used for training. This dataset is upsampled to 100 times following the default setting of the code of Chaabouni et al. (020). The learning rate is set to 0.001, which also follows Chaabouni et (2020).. Based on our preliminary experiments to explore stable learning, a sender S and a receiver are trained for 200 epochs, and the coefficient of the entropy regularizer is set to 0.01.
Boundary Detection Algorithm The boundary detection algorithm involves a parameter threshold. Since the appropriate value of threshold is unclear, vary threshold as follows:
threshold E {0, 0.250.5 0.5, 0.75, 1, 1.25, 1.5, 1.7
5.2 IMPLEMENTATION NUMBER OF TRIALS, AND LANGUAGE VALIDITY
We implemented the code for training agents using the EGG toolkit KKaritonov et al. 2019 10 EGG also includes the implementation code of Chaabouni al. (2020), which largely refer Each run took a few hours with a single GPU. In the following sections, an emergent language with communication success rate of more than 90% is called successful language.
RESULTS
As result of training agents, obtained 7,8, 6, 8, 7, and 6 successful languages of runs for configurations (Mast, Rval) (1, 4096), (2, 64), (3, 16), (4, 8), (6, 4), and (12, 2), respectively.
6.1 CONDITIONAL ENTROPY MONOTONICALLI DECREASES
To verify Q1 we show the conditional entropy ((n) Eq. 2 in Figure In Figure 3 the condi- tional entropies of the successful languages (solid red lines) decrease monotomically This confirms Q in successful languages. Interestingly, the conditional entropies of emergent languages derived
"The EGG toolkit: http://s//sttaaaac////eoooooooee
code: http://hhh////ooooooooooooo/oooa

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3,0
Emergent language before training
25
2.5
Successful language after training
PCL A..
2.0
1.5
1.
10
0.5
0.0
0
5
10 15
20
25
30
tranatiinn boundary!
toanstion hounddyy
tonsiioon for boundory2
Figure Conditional entropy (n). Dashed Figure blue lines represent (n) of languages from un- trained agents that finally learned successful lan- 43,8,4....,44444" guages, while solid red lines represent H of successful languages.
Example transition sequences of the branching entropy h in message successful language for ([att; 64).
from untrained senders do necessarily decrease, shown as dashed blue lines in Figure 3 The monotonic decrease in conditional entropy emerges after training agents.
6.2 BRANCHING ENTROPY REPEATEDLY FALLS AND RISES
Next, to answer Q2 we computed the branching entropy h(s) (Eq. 1) of the successful languages and applied boundary detection. As example, show a few actual transitions of h((s) in Figure 4 in which the y-axis represents the value of ((()) and the x-axis represents a message "3,8,4... .9.8,4....44444 4,4.4
The message is randomly sampled from successful language when (Natt; Ryaa) (2, 64). The boundary detection algorithm with threshold = 1 yields three hypo-boundaries that are represented dashed black lines in Figure 4 Blue, yellow, and green lines with triangle markers represent the transitions of h(s) that yield hhpo- boundaree....iss..... Note that the (i 1)-th transition of h(s) does not necessarily start from the i-th hypo coyoobbuundar, due to the definition of the algorithm. For instance, the second transition overlaps the first hypo-boundary. While the conditional entropy decreases monotonically as shown Figure 3. the branching entropy repeatedly falls and rises in Figure 4 Moreover, we show the mean number of bond-bounssiees per message in Figure 5) Figure 5 cates that for any (Natt, vla)) configuration, there are hypo-boundaries threshold < 2, i.c. the branching entropy repeatedly falls and rises. These results validate Q2
6.3 HYPO-BOUNDARII MAY NOT BE MEANINGFUL BOUNDARIES
Next, investigate whether Q3-1 Q3-2 following sections falsify them. Thus, Q3
and Q3-3
hold in successful languages. The results in
may not true: hypo-boundaries may not be
meaningful boundaries.
Mean Number of Elypo-Boundaries per Message the mean number of hypo-boundan boundariss per message does not increase nat increases. It does not decrease, either. This result falsifies Q3-1 Even when Natt = 1, there are as many hypo-boundaries other configurations.
See Figure 3) again. The figure shows that
Vocabulary Size Figure 6/oows the mean vocabulary sizes for each (Natt, Rval). The vocabulary size does increase as Doaal increases, which falsifies Q3-2 However, focusing on (natt: Rlval) {(2, 64), (3, 16), (4, 8), (6, 4)} and 0.25 < threshold S there is a weak tendency to support C2
suggests that hypo-segments are completely meaningless either.
One might think that the conditional entropy cannot increase by its definition. However, this is not the case setting (see Appendix B for more details).

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Mean # Ilyoo boundaries Messags
threshold =
7000
threshold =
20
threshoid - 0.25
ahreshold = 0.25
threshold - 0.5
threshold 0.5
15-
threshoid
5000
threshold 0.75
threshold =
Vocabulary
4000
threshold
10
threshold = 15
threshold 1.25
8000
threshold = 1.5
threshold 1.5
5-
threshold = 1.75
2000
threshold 1.75
threshoid
1000
threshold =
4.40966
(2, 64)
(3, (3.16)
16. 6..4)
(12.21
a1 4096)
(2,64)
(3. 161
(4.8)
0.41
(2.2)
(4,
(Pam (rai) Configuration
(ua) Configuration
Figure 5: Mean number of hypo-boundaries message in successful languages. threshold varies according Eg. 7 Each data point is averaged over random seeds, and shaded regions represent one standard error of mean (SEM).
Vocabulary size in successful lan- guages. threshold varies according to Eq. 7 Each data point averaged over random seeds, and shaded regions represent one SEM.
Figure 6:
0.12
(eat Aoal = 12, 64)
[Patt: Rived = (2,64)
0.4
(Mat, = [3,16] 16)
0.10
[Nan, Rival (2, 64) (random boundary)
MMan Auol 14,
(Matn Aow) = (6,
0.08
TopSim
(eam, Rowl (12.2)
TopSim
0.06
0.02
0.00
0
0.25 0.5
0.75
1.55
1.5
1.75
0.0
0.5
1.0
1.5
2.0
threshold
threshoid
Figure 7: C-TopSim and W-Toppim in success- ful languages. "-oo" corresponds to C-TopSim, while others correspond to W-TosSim. Each data point is averaged over random seeds, and shaded regions represent one SEM.
Figure Hypo-boundary-basee W-TopSim compared to random-boundary-based W-TopSim in successful languages for (Natt: Rval) (2, 64). Each data point is averaged over random seeds, and shaded regions represent one SEM.
C-TopSim vs W-TopSim Figure shows C-Topsim and W-Topsim for each (natt Rvaa) and threshold Note that C-TopSim TopSim with "character" edit distance, and W-TopSim is Top- Sim with "word" edit distance. In Figure 7 threshold = -o0 corresponds to C-TopSim, while the others correspond W-TopSim. Our assumption in Q3-3 was C-TopSim W-TopSim. On the contrary, Figure shows clear tendency for C-TopSim > W-TopSim, which falsifies Q3-3 Hypo- boundaries may be meaningful. However, they may be completely meaningless either. This is because the grpo--boundry-bbaee W-TopSim is higher than the
boundary-based W- TopSim Figure 8 Here, we mean by random boundaries the boundaries chosen randomly in the same number as Syypo-boundaries in each message. Other (Natt, Real) configurations show similar tendencies (see dpeeAddedi C).
6.4 FURTHER INVESTIGATION
Setting Does Not Contribute HAS Methods proposed improve TopSim might also contribute to giving rise to HAS. We picked up the Ease-of Teaching (EoT, Li & Bowling, 2019, as it known to simple and effective way to improve TopSim. However, the FoT setting did not contribute to the emergence of HAS, while improving C-TopSim (see speeAndpddix D).
12 Note that TopSim can only he defined when nat Iff we were to apply boundary detection with threshold = -o, it would regard every data point in message a boundary. In other words, W-TopSim with threshoid
would be identical to C-TopSim. We adopt this notation order represent C-TopSim and W-TopSim unified manner in single figure.
8

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Word Length and Word Frequency The results far are related to compositionality of emergent languages (c.g.. Kottur et al. 2017). We further associate our results with previous discussions on Zipf's 's law of abbreviation (ZLA) emergent languages in Appendix E
7 DISCUSSION
In Section 6.1 we showed that the conditional entropy (n) decreases monotonically in emergent languages, confirming Q1 In Section 6.2 we demonstrated that the branching entropy h(s) repeat- edly falls and rises in emergent languages, which confirms Q2 It is an intriguing result, considering the discussions of Kharitonov et al. (2020), who showed that the entropy decreases to the minimum for successful communication if the message length k 1. In contrast, our results suggest that the (branching) entropy does not simply fall to the minimum when the message length & longer. How- ever, in Section 6.3] our results indicate that the hypo-boundaries may not be meaningful since Q3-1 [3-2] and Q3-3 were falsified. Nevertheless, hypo-boundaries may not be completely meaningless W- W-Toppim is higher than the mundom-boundayy based W-TopSim. suggests that HAS- based boundary detection worked to some extent.
either. This is because the hypo--bundaryy
One limitation of this paper is that focused on the specific neural architecture, i.c.. GRU (Cho ctt al. 2014) as an agent basis, following Chaabouni et al. (2020). remains to be answered whether articulation structure may arise in other architectures. Another limitation is that
WAAS-based criteria highly relies on attribute-value settings, which obscures how to apply them to other settings, e-g., visual referential games (Havrylov & Titov [017 Choi et al. 2018). At least, however, C3 is applicable to the referential game, by using cosine dissimilarity between hidden states (Lazaridou al. 2018). Finally, the question of why the current emergent languages fail to give rise to HAS left open.
8 RELATED WORK
Segmentation of Emergent Language RResnick et al. (2020) proposed compositionality mea- sure called residual entropy (RF) that considers the partition of emergent messages, akin to the segmentation in this paper. However, RE is different from our HAS- based criteria in the following sense. First, RE assumes that all the messages are partitioned in the same way, i.c., boundary posi- tions are the same in all the messages. Second, the computation of partition involves an explicit reference attribute values. This is opposed to HAS' statement that boundaries can be estimated without reference to meanings. Third, RE can be hard to compute 14
Unsupervised Word Segmentation Regarding unsupervised word segmentation, this paper fo- cused on HAS, for its simplicity, interpretability, and linguistic motivation. Of course, however, sta- tistical models for word segmentation have made progress Goldwater al. 2009 Mochihashi ct al. 2009). In addition, Taniguchi et al. (2016) tried to discover the double articulation structure (Martinet 1960), i.e., both phonemes and words, from continuous speech signals. As emergent lan- guages become more complex and natural- language- like in the future, it will be worthwhile to adopt more sophisticated segmentation methods.
CONCLUSION
This paper investigated whether Harris's articulation scheme (HAS) also holds in emergent lan- guages. Emergent languages are artificial communication protocols emerging between agents. HAS has been considered statistical universal in natural languages and can be used for unsupervised word segmentation. Our experimental results suggest that although emergent languages satisfy some prerequisites for HAS, HAS-basee word boundaries may not be meaningful. Our contributions (1) to focus on the word segmentation of emergent languages, (2) to pose verifiable questions to an- whether emergent languages have meaningful segments, and (3) to show another gap between emergent and natural languages. The standard signaling game satisfies prerequisites but still lacks some necessary ingredients for HAS.
"We have to compute entropy values for all the possible partitions to finally obtain RE. In our case, the number of possible partitions in one language can be astronomical up to Batt" 12 3.4 x 1034.

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ACKNOWLEDGMENTS
We would like to thank the anonymous reviewers for fruitful discussions and insightful comments.
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A IDEALLY COMPOSITIONAL SYNTHETIC LANGUAGE INDEED SATISHIES CONDITIONS
AND C3
The purpose of this supplemental section is show that the HAS-based boundary detection algo- rithm works for an ideal language and that the ideal language indeed satisfies conditions CT and
A.1
SETUP
Recall that att is the number of attributes, Tlyal is the number of values, attribute value set Diaat is meaning space, and (Natt: Rval) E {(1, 4096), (2, 64), (3, 16). (4, 8), (6, 4), (12, 2)}. Set the alphabet size |A| (same as the main content). Set the message length 36 (slightly different from the main content, but has good property in the sense that k mod Mart 0 for all Nat.). Make an ideal synthetic language
following procedure:
Generate random segments $a, = x2 all E {1,... Nat } and u E {l,.... R.al). Assume that each 8,,v denotes each attribute-value meaning unit: the value v of the attribute a.
k//aal by x Uniform(A) with replacement for Ba.t
Generate synthetic messages m(U)....: Unars) $1,0 $2,02 Saat by concatenating the segments $j.yy. Assume that each m(f1,.... Unan) denotes the corresponding attribute-value ob- ject (,1...., Unnaa) E Dilate.
Create synthetic language L = {m(u1.... Unams (U1,... Uniaet ) E D
A.2 RESULT
According to Table All of
and C3
are satisfied in the ideal synthetic languages. We also Figure 10 in the same manner as the main content.
show the results of Table
Figure
Ratt
Tvaal
#boundaries
vocabulary size
C-TopSim (undefined)
W-TopSim
4096
1.52 (#:0.01)
9407.1 (#:37.8)
(undefined)
2
64
2.68 (#0..0)
394.8 (#7.9)
0.134 (+0..01)
0.465 (+00.01)
3
16
3.64 (#:0.15
96.8 (#:3.2)
0.201 (#:0.055
0.465 (+00.01)
4
8
4.91 (#0.19)
62.2 (+2.6)
0.227 (#0.009)
0.627 (+0.021)
4
7.02 (#0.19)
39.7 (#1.3)
0.253 (#0.013)
0.763 (#0.015)
12
2
11.31 (+0.43)
28.2 (+0.8)
0.256 (+00.08)
0.862 (+00009)
Table 1: The mean number of hypo-boundaries per message, the vocabulary size, C-TopSim, and W-TopSim of ideal synthetic languages. We obtained eight languages for each (Natr Raal) with different random seeds. (#: represents the standard error.
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x streshols 0.5
Newn Mypo lour dariin Mexuare
Woah Etee
64
1.18
014400
64)
06.2
14
18
Confgrazion
Con.
Figure 9: Mean number of hypo-boundaries per message in synthetic languages. Each data point is averaged over random seeds, and shaded regions represent one standard error of mean (SEM).
Vocabulary size in synthetic lan- guages. Each data point is averaged over random seeds, and shaded regions represent one SEM. The y-axis is on the log scale, as the datapoint for (Mats, Rlaal) = (1, 4096) is very large com- pared to the other datapoints.
Figure 10:
Ohe.tal (3.10)
(hg/fall14. 16.4)
Ohcefal -6.40
Toddin
Strwchold
Figure 11: C-TopSim and W-TopSim synthetic languages. "-oo" corresponds to C-TopSim, while other threshold values correspond to W- TopSim. Each data point is averaged over dom seeds, and shaded regions represent one SEM.
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B WHY CAN CONDITIONAL ENTROPY INCREASE IN SIGNALING GAME?
One might wonder why the conditional entropy (n) can increase and think it cannot due to its def- inition. This is true when we have a single (possibly infinite) sequence. For example, the conditional entropy of an infinite monkey typing sequence is constant since for any n E N and E X
h(s) -- P(a 5) log, P( ()
Σ | ZEN
loga |
log2 |
ZEN
H(n) Σ P(s)h(() logg x *EXT
In general, the conditional entropy (n) of a single sequence is weakly decreasing function. However, emergent languages arising from signaling games are single sequences. Each emergent language is a set of finite sequences: L = (S(() E M E Z}. Consider, for instance, the following toy language:
anaca
Lay
anaab
anaac
In Looy (1) (4) holds, symbol after unigram is most likely be a, while symbol after a 4-gram is equally likely to be b, and c.
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C HYPO-BOUNDARY-BASED W-TOPSIM AND RANDOM-BOUNDARY-BABER W-TOPSIM
0.175
(Mags Pual) - (3, 16)
0.20
(Pam Dyall [4
(Nam flva) 13, 16) (random boundary)
(4,8) (random boundary)
0.125
0.15
TopSim
0.100
TopSim
0.075
0.05
0.025
0.000
0.00
0.0
0.5
1.0
1.5
2.0
0.0
0.5
1.0
1.5
2.0
chreshold
Uireshoid
Figure 12: Hypo-boundary-based W-TopSim
Figure 13:
Hyyoo-boundary-based W-TopSim
compared to random- bonton--ooddrr--aseee W- W-Toppim in successful languages for (Natt: Rval) (3, 16). Each data point is averaged over random seeds, and shaded regions represent one SFM.
compared gandoomboundary-ae in successful languages for (Natt nvaa) = (4, 8). Each data point is averaged over random seeds, and shaded regions represent one SEM.
Wo
Mage Ovai) (6
0.30
[Rati Fiva) - (12,2)
0.25
(Taas Niaa) (6.4) (random boundary)
[Pati Rive) = (12, 2) (random boundary)
0.25
0.20
TopSim
0.15
TopSim
0.15
0.10
0.10
0.05
0.05
0.0
0.5
1.0
1.5
2.0
0.0
0.5
1.0
1.5
2.0
threshold
threshoid
W-TopSim compared to random- random-boundee boooo--boddrrybbasee W-TopSim compared to bonddom-bbundrr--baedd W-TopSim in successful languages for (Nattt rhva)) = (6, 4). Each data point is averaged over random seeds, and shaded regions represent one SEM.
Figure 14: buso-b W- W-TopSim
Figure 15:
in successful languages for (natt: Raal) (12, 2). Each data point is averaged over random seeds, and shaded regions represent one SEM.
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D EASE-OF-TEACHING SETTING DOES NOT CONTRIBUTE TO HAS
Some methods proposed to improve TopSim might also contribute to the emergence HAS. We picked up the Ease-of-Teaching (EoT) setting (Li & Bowling 2019), as it known to be simple and effective way to improve TopSim. As a result, the BoT setting did not contribute to the emergence of HAS, while improving TopSim (C-TopSim).
Setup The EoT setting is quite simple: it resets the receiver agent periodically during training time. In this paper, we reset the receiver every 20 epochs.
Result We show the results in Figure 16 Figure 17 and Figure 18
The mean number of hypo-
boundaries does not increase as Matt increases in Figure 16 The vocabulary size does not increase
1lval increases in Figure 17. C-TopSim W--TopSim in Figure 18
Mean # typo-boundaries per Message
threshold 0
20
d
9000
threshold 0.25
threshold=
threshold 0.5
threshold
threshold
threshold
Vocabulary
threshold =
dhreshold 1.25
threshold 1.25
10
threshold 1.5
4000
Vireshold = 1.5
threshold 1.75
tiresholf 1.75
threshold=
2000
2
(1.4096)
(2,
(616))
(4.8)
(12. 2)
(1. 4096)
(2, (2.64)
(3.16)
(4, 8)
(6.4)
(12, 2)
( Pvail Configuration
Daal Configuration
Figure 16: Mean number of hypo-boundarres per message in successful languages with the HoT setting. Each data point is averaged over random seeds, and shaded regions represent SEM.
Vocabulary size in successful lan- guages with the EoT setting. Each data point is averaged over random seeds, and shaded regions represent SEM.
Figure 17:
0.6
(Patz Pay) - 64)
0.5
(Paz Pay) - (3.
(aa Paai = (4.8)
0.4
(Pams Pad) 4)
TopSim
(Pam Pay) (12,
0.25 0.5
0.75
1
1.25
1.5
1.75
2
threshold
Figure 18: C-TopSim and W-TopSim in success- ful languages with the EoT setting. "-oo" corre- sponds C-TopSim, while other threshold val- ues correspond to W-TopSim. Each data point is averaged over random seeds, and shaded regions represent one SEM.
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Published as a conference paper at ICLR 2023
E HYPO-SEGMENTS AND ZIPF'S LAW OF ABBREVIATION
The results in the main content are related to compositionality of emergent languages (c.g., Kottur ct al. (2117) In this supplemental section, we further associate results with previous discussions on Zipf's 's law of abbreviation (ZLA) in emergent languages (Chaabouni et al. 2019 Rita et al. 2020 statistical property In natural languages that the more frequently a word is used, the shorter it is (Zipf) 1935). By considering hypo-segments as "words," can check whether hypo-segments follow ZLA. Figure shows the hypo-segment lengths sorted by frequency rank for (Natt, Ryal)) = (1, 4096) T5 hypo-segments follow ZLA idcally, they should show a monotonic increase. The distribution of the lengths of the hypo-segments shows clear 7LAA-like tendency for threshold E (0, 0.5), although the tendencies are less clear for the other threshold [6] means that hypo-segments follow ZLA with an appropriate threshold value. Other (Natt, (vaal) configurations show similar tendencies (see Figure 20
[eda & Washio 2021). ZLA is known
Figure 21 Figure 22
Figure 23
and Figure 24).
This is suggestive result because we neither imposed length penalty messages (Chaabouni al. 2020), modeled the laziness/impatience of agents (Rita et al. 2020), nor modeled short-term memories (Ueda & Washio 202I). Note that it may be just artifact, analogous the fact that even a monkey typing sequence divided by the "white space" follows ZLA (Miller 1957).
hypc-segment Length
Hypc-segment Length
104-
10
100
10a
10a
10
10a
10a
10
102
10a
Frequency
Frequency
(nsex Nval) (1, 4096), threshold =
(Nua: Pral) = (2, 64),
=
ingex (uai) = (1, 4096), threshold 0.5
(Nadi Ryal) = (2. 64), threshold 0.5
(ngex Ivail 4096), threshold =.5
(Nua: Rral) = (2. 64), threshold' =15
(Nue, hra)) 4096), threshold
(Nua: Pra)) 64), threshold
Figure 19: Hypo-segment lengths sorted by fre- quency rank for (Natz: Vivi)) 4096.. Each data point is averaged over random seeds, and shaded regions represent one SEM.
Figure 20: Hypo-segment lengths sorted by fre-
quency rank for (Natt, Rvai) = (2, 64). Each data point averaged over random seeds and shaded regions represent one SEM.
Banindd Seaaaaai Length
peo-saagaan Length
10a
Hype
10a
10a
100
10
10a
10
10a
10
102
10
Frequency Rank
Frequency Rank
(Nus, Aual) 13, 16), threshoid
[Man. Gual) (4, 8), threshold =
(Nus, flaa) (3, 16), threshold 0.5
Mat. feaa) (4, B), threshold 0.5
(Mus, fual = 16), threshold=
Mat.. freal (4, B), threshold
(nes, (ew. Aowl = 16), threshoid==
Maas, fical = (4, 8), threshold =
Figure 21: Hypo-segment lengths sorted by fre- quency rank for (natt, Phva) = (3, 16). Each data point is averaged over random seeds and shaded regions represent one SEM.
Figure 22: Hypo-segment lengths sorted by fre-
quency rank for (Natt, Plval) = (4,8). Each data point is averaged over random seeds and shaded regions represent one SEM.
We picked up only threshold e (0.5, 1.5, 2} and adopted log-log graph for readability. 16e plot shows the zigzagging behavior for threshold = 1.5, and most of the hypo-segment lengths hit the message length & 32 for threshold 2.
19

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Published as a conference paper at ICLR 2023
Sppceegiinn Length
ppp--eement Length
104
10-
10
10*
10a
10
10a
10a
10"
Frequency Rank
Frequency Rank
(Pan: Pvar)
threshold
Pral) (12. threshold
(fam (vaa)) 4), Mhreshold 0.5
(Nuz) Prai) (12. 2), threshold 0.5
(Mam Pva)) = (6, 4), threshold = 1.5
(Nudi Pral) = (12. 2), threshold = 1.5
(Nan (va)) (6, 4), threshold =
(Nua. Pra)) (12. 2), threshold'
Figure 23: Hypo-segment lengths sorted by fre- quency rank for (Nast. fvval) = (6, 4). Each data point is averaged over random seeds and shaded regions represent one SEM.
Figure 24: Hypo-segment lengths sorted by fre-
quency rank for (Natt, Aval) = (12, 2). Each data point is averaged over random seeds and shaded regions represent one SEM.
20

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Published as a conference paper at ICLR 2023
F ILLUSTRATION OF BOUNDARY DETECTION ALGORITHM
When 0, for w 1, 2, 3,
Boundary
h h
h
Message
W
W 2
W 3
Likewise, for = 0, 1, 2, ....
Boundary
Boundary
Boundary
Boundary
h
Message
i i=
= 1
1=2
Aggregate boundaries
Segments
Figure 25: Illustration of boundary detection algorithm (Algorithm) 1
21


Reviews:
[{"review_id": "pg_5jbOVgU", "reviewer": "Reviewer_nRa3", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper analyzes the articulation structure that could emerge in emergent language. The paper follows the studies of deep learning-based emergent communication models, which have been studied for half of the decade. In particular, the study adopts the framework of Chaabouni et al. (2020).\nThe authors tested if they satisfied HAS. HAS states that word boundaries can be obtained solely from phonemes in natural language. We adopted  HAS-based word segmentation and verified whether emergent languages have meaningful word segments. The experiment suggested they do not have. They found that the current emergent language model is still missing some necessary ingredients.\n", "summary_of_the_review": "\nThis paper analyzes the articulation structure that could emerge in emergent language. The paper clearly shows that articulation structure, HAS, does not emerge in the sense of entropy-based measure in a type of emergent communication. However, because of the lack of theoretical insight of the architecture of emergent communication, the value of the obtained insight is limited to the model they adopted. It means this is a kind of \"case study.\" As a result, I am not sure if the paper contains a sufficient contribution to appear as an ICLR paper. \n\n\n[Update] Considering the authors' revision, I have updated my score.", "strength_and_weaknesses": "Strength\n+ The paper clearly shows that articulation structure, HAS, does not emerge in the sense of entropy-based measure in a type of emergent communication  \n+ The paper provides an interesting suggestion about the limitation of the current emergent communication model.\n\nWeakness\n- The paper did not discover the reason why the current emergent communication model does not make HAS emerge.\n- Apparently, the property depends on the implicit definition of language models that are used in emergent communication models. This analysis is just about a type of emergent communication model and architecture.\n\n\n\nAlso, regarding word segmentation, the authors have mentioned HAS and Tanaka-Ishii studies. However, statistical models for word segmentations have made progress. The author refers to such studies. Also, even simultaneous phone and word discovery statistical models have been proposed.\n\n[Word discovery]\n[1] S. Goldwater, T. L. Griffiths, and M. Johnson, \"A Bayesian framework for word segmentation: Exploring the effects of context,\" Cognition, vol. 112, no. 1, pp. 21–54, Jul. 2009. \n[2] D. Mochihashi, T. Yamada, and N. Ueda, \"Bayesian unsupervised word segmentation with nested Pitman-Yor language modeling,\" in Proc. Joint Conf. 47th Annu. Meeting ACL 4th Int. Joint Conf. Nat. Lang. Process. AFNLP (ACL-IJCNLP), Singapore, 2009, pp. 100–108\n\n[Phone and word discovery]\n[3] Tadahiro Taniguchi, Shogo Nagasaka, Ryo Nakashima, Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals, IEEE Transactions on Cognitive and Developmental Systems, Vol.8 (3), pp. 171-185 .(2016) DOI: 10.1109/TCDS.2016.2550591 (Open Access) \n\nYou may be interested in word segmentation with co-occuerence cues in relation to \"meaningful\" segments.\n[4] Tomoaki Nakamura, Takayuki Nagai, Kotaro Funakoshi, Shogo Nagasaka, Tadahiro Taniguchi and Naoto Iwahashi, Mutual Learning of an Object Concept and Language Model Based on MLDA and NPYLM, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'14), 2014\n\nAs mentioned above, the phenomenon of the emergence of word segments depends on the implicitly designed language model, i.e., GRU in this model. Therefore, it is essential to clarify this point in the discussion of the general nature of the possibility of the emergence of articulation structure.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "QCoBNZEm-pV", "reviewer": "Reviewer_zwe8", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "\nThis paper studies the languages that emerge between two agents that solve a task collaboratively. The main focus of the paper is on studying whether Harris’ articulation scheme (HAS) holds in the emergent languages. To test for that, the authors resort to an unsupervised segmentation algorithm derived from HAS. At its foundation, this algorithm compares the branching entropy before and at a position; it is assumed that at boundaries it starts growing.\n\nNext, the problem of testing whether HAS holds is reduced to testing whether (Q1) Conditional entropy of the next symbol given the prefix is decreasing, (Q2) Branching entropy increases and decreases, (Q3) Derived boundaries are “meaningful”. (Q3) is further split into testing if the boundaries behave as expected when the number of attributes and values are changed.\n\nThe experimental study is performed using a standard setup where the sender agent receives a n_attr x n_val dimensional vector encoding a point in an attribute-value space with n_attr attributes each having n_val values. The sender then sends a multi-symbol message to the receiver agent which has to recover the original vector. Both agents receive a reward.\n\nThe experiments show mixed outcomes: (Q1) and (Q2) are indeed observed while the authors find the boundaries do not have the expected properties w.r.t. changing the number of attributes and values. Finally, the paper studies the relation between the Ease-of-Teaching and HAS and whether “words” obtained under segmentation follow ZLA.\n", "summary_of_the_review": "Generally a clear paper with interesting experiments. My main comments can be summarized as:\n* At this stage it is obvious that emergent languages are far from human languages from many points of view. Why is finding this particular difference useful?\n* How studying word segmentation differentiates from studying compositionality of the emergent languages?\n* Segmentation assumes that “words” are composed of adjacent tokens. Is it a natural assumption for the communication between artificial agents?\n", "strength_and_weaknesses": "Strengths:\n* I believe the paper brings an interesting toolset to the topic of analyzing emergent languages\n* The experimental study is convincing\n\nWeaknesses:\n* Motivation: We already have a strong understanding that human language-like, interpretable features (ZLA, compositionality) often do not emerge by themselves in emergent languages. For now, looking at HAS is motivated as yet “another direction” to study emergent languages. I think the paper could benefit from highlighting why it is particularly worth studying. Furthermore, If HAS is important, can we take the findings of the paper and find a way to enforce it?\n* In my view, the search for “word” boundaries seems to be closely related to the search of compositionality, i.e. whether there are elementary units within messages with a fixed “meaning”  (Chaabouni et al., 2020). I believe the text could benefit from contrasting those pursuits. For instance, if compositionality in the sense of (Chaabouni et al., 2020) is achieved, would word boundaries become meaningful? or vice-versa? \n* Further, as formulated, the word boundaries segment the messages into groups of adjacent tokens (ie. the 2nd token cannot be grouped with the 4th token w/o being grouped with the 3rd token). Is there an intuition why this should hold for the artificial agents? As far as I understand, any successful language can be modified by a fixed permutation of positions w/o sacrificing its expressiveness. For instance, the compositionality metrics of (Chaabouni et al., 2020) are specifically made permutation-invariant wrt positions to reflect this. Moreover, whether adjacent tokens tend to be related is likely architecture-dependent, e.g. while this might hold for LSTM and GRU cells which have “recency” bias, it most likely not hold for Transformers which are permutation invariant (modulo positional embeddings). \n* Eq (3) does not include conditional entropy. Do we need it monotonically decreasing (Q1) for HAS to hold?\n* The paper shows that the word boundaries do not behave as one would expect and thus concludes that they are not “meaningful”. I wonder if it makes sense to check if, among the studied languages, there are individual words that encode specific attributes or values?\n* Minor:\n   * Typo: S2.2, first para: “HAS-baseThisboundary” ->  “HAS-base. This boundary”\n   * I believe the text does not specify what kind of reward the agents get. Is it cross-entropy of the receiver’s output?\n\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "Nk2njK9oKCF", "reviewer": "Reviewer_XTS9", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes a method for doing word segmentation in emergent languages derived during a one-way signaling game. The method is based on Harris’s articulation scheme, and involves tracking a branching entropy term for a rapid rise. The method is light-weight and can be derived from simple surface (n-gram) statistics, and has been used in a series of previous word segmentation papers. Perhaps more importantly, the authors propose a set of criteria (C1-C3 in Section 4) to determine whether meaningful word-like units have been discovered. This is nothing specific to the Harris articulation scheme in these criteria, and they could likely be used to assess other segmentation methods or other communication-game learning methods for meaningful words. The conclusion of evaluating the proposed method with these criteria is that the preconditions for this method have been met, but there is as of yet no evidence that meaningful units have been discovered.", "summary_of_the_review": "This paper feels a little niche, both in that I do not have a strong notion whether the word formation problem is an important one in emergent languages at this point, nor do I know whether this proposed technique is the best approach for finding word boundaries if they do exist. Regardless of the nicheness of the problem, the paper is well-written, and builds a strong evaluation framework for other groups to use, validating that framework with a synthetic language in Appendix A. The authors use this framework to draw the conclusion that for this configuration of the signaling game, no meaningful units are emerging, which definitely raises some interesting questions.", "strength_and_weaknesses": "Strengths:\n\nThis is a very careful, thorough paper. Everything is carefully defined and described. It is dense, but ultimately easy to follow.\n\nThis paper has two paths to influence: one by reminding the community of this method and applying it to emergent languages, and one by defining criteria that can be used to assess the meaningfulness of word-like units discovered in emergent languages.\n\nThe inclusion of an assessment of a synthetic language in Appendix A is a really nice touch, and greatly strengthens the paper, providing confidence that the conditions do indeed work for languages that verifiably have meaningful words.\n\nWeaknesses:\n\nIt is unclear to me how important word formation is in current studies of emergent languages. As the authors themselves point out, most previous work implicitly assumes one-character-per-word. \n\nAs this appears to be the first paper to dive into word formation, it would be interesting to talk about the ramifications of some of the changes they’ve made to the set-up. Did forcing multiple-character representations reduce the success rate or the scores of successful systems? Are there any downsides to using fixed-length messages when hoping for meaningful words? I.E. Couldn’t the system then assign meanings to certain characters in certain positions? The paper mentions only experimental benefits of fixed-length messages.\n\nThe work is very specific to this particular technique. It has the feel of an author dusting off a favorite tool and aiming it at a new problem. I worry that this will cap the amount of interest this paper can draw from the emergent language community. In my review, I have tried to emphasize the portions of the paper that apply to any segmentation scheme. I think it would benefit the paper for the authors to do the same.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "5EhNet9YQ8", "reviewer": "Reviewer_PmT4", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper investigates word boundaries in the context of emergent languages. The hypothesis is that if we want emergent languages to follow properties of natural languages like compositionality, they should have meaningful word boundaries as defined by Harris Articulation Scheme. The authors propose derived tests to check whether a given emergent language possess this property. They show that factors encouraging compositionality in emergent languages do not correlate with languages having this property.", "summary_of_the_review": "The paper is still a work in progress and I would encourage the authors to bolster their claims by adding diverse experiments and a more thorough literature survey adding comparisons with prior work.", "strength_and_weaknesses": "The paper proposes a new research direction/benchmark for emergent languages to be as close to natural language. The direction is indeed interesting and important for the research on emergent languages as the main goal is to achieve human interpretable languages.\n\nHowever, the analysis done in the paper is more quite limited to come to a definite conclusion. The only environment chosen to perform the tests is the reconstruction game which basically just emulates the encoder-decoder setup. I would encourage the authors to carry out similar tests for referential games and other derived games that have been used in emergent communication literature.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "2Ch0km1KbK", "reviewer": "Reviewer_BukJ", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "Zellig Harrs's articulation scheme (HAS) is an unsupervised method of discovering word segmentation based on the statistics of sub-word units in natural language.\nThis paper takes uses HAS on emergent language to determine whether the statistical properties of sub-word units in emergent language behave similarly to natural language.\nThe experiments show that while some of the low level statistical properties which HAS predicts are present, the evidence for HAS discovering meaningful word boundaries in emergent languages tested is largely negative.\n", "summary_of_the_review": "I recommend accepting the paper because it applies a linguistic framework in natural language to emergent language which (1) is genuinely useful to studying the structures of emergent language and (2) is well-suited to the constraints which emergent language presents (e.g., few a prior assumptions of structure, comprising arbitrary symbols).\nThe experiments clearly test the hypotheses, and the empirical results are generally informative.\nThe places where the results do lack do not significantly detract from the aforementioned strengths.\n\n\n### Misc.\n- `2.2`: First sentence of 2.2 needs to be checked for typos.\n- `2.2`: The little interlude about applying HAS-based boundary detection to natural language does not contribute much on its own.\n  It can probably be removed if there is no further discussion of HAS applied to natural language.\n", "strength_and_weaknesses": "### Strengths\n- [major] Unsupervised word boundary detection is genuinely useful to studying the structures of emergent language and has not been tackled before.\n- [major] The hypotheses and corresponding experiments are clear and effective.\n- [major] This paper applies a concept from linguistics to emergent language in a fitting and relevant way.\n- [minor] The empirical results of the experiments are informative vis-a-vis the hypotheses, although the results are not definitive or earth-shattering.\n### Weaknesses\n- [minor] No quantitative measure of branching entropy falling and rising\n- [minor] Tough to put the empirical results in perspective due to lack of natural language/random language baseline or ablation study.\n  Appendix A addresses this from one angle and helps make this a only a minor weakness.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: poster