Descripción de las interacciones del sub sistema apícola con los demás componentes del sistema de producción campesino del municipio de Ocamonte

Our second goal is to understand more precisely how such experiments can be useful for linguists, beyond the benefit of having access to better automatic word segmentation tools for their data.

Phonological status of complex consonants In the analysis of the results (Sec-tion 4.4.1 above) we showed the benefit of integrating digraphs or trigraphs in the consonants inventory for Mboshi. This result is of special interest for linguists, since it is in line with the most recent phonological analyses of Mboshi (Embanga Aborobongui, 2013;Kouarata,2014;Amboulou,1998) which agree in recognizing complex consonants

10https://homepages.inf.ed.ac.uk/sgwater/resources.html.

11Results for dpseg or pypshmm are averaged over two runs.

reference a i e o le la laa lii lee loo baa bii boo maa mo moo mi mii yee discovered a o i e la waa laa mo yee le ya ngai mi ř sa ma baa lo lii me Figure 4.6: An example of the 20 most frequently found prefixes in Mboshi, using gram-mar A3-B4-C3-D1 (no supervision). True discovered prefixes, present in the reference, are underlined.

(represented by digraphs or trigraphs) in the phonological inventory of this language.

The analysis of complex consonants, in particular prenasalized consonants, has gener-ated many debates in Bantu linguistics (Odden,2015;Herbert,1986;Downing, 2005);

the present experiments provide more substance to support the integration of complex consonants in the phonological inventory of Mboshi.

Learning prefixes without supervision Since parses are produced to segment sen-tences into words, it is possible to extract the most frequent prefixes or suffixes (for B variants introducing such a concept). The precision on the 20 most frequently found prefixes for grammars without prefix-supervision (B3, B4, B5 and B6)¹²reaches 58.21%

in Mboshi, and 61.21% in Myene. An example of discovered prefixes is shown in Fig-ure 4.6. The capacity of AGs to learn true prefixes without supervision can thus help linguists in the process of documenting a new language.

On the supervised variants (B4+, B5+, and B6+) including rules corresponding to prefixes in Mboshi (e.g. “Prefix→ a”), the average precision achieved in Mboshi is 61.11%, and 63.07% in Myene: the benefit of the supervision is limited. Yet, it holds for all B variants with a concept of prefix, as shown in Figure 4.7.¹³ Token metrics for

Figure 4.7: Precision on the 20 most frequently found prefixes in Mboshi and Myene for hypotheses B3, B4, B4+, B5, B5+, B6, and B6+ (precision is averaged over all A, C, and D variants).

Mboshi with the supervised variants also indicate a benefit for word segmentation (WF in Figure 4.3, top). It is important to note that the supervision provided in B4+, B5+,

12We include B3 variant, interpreting its non-terminal Prefixes as a prefix.

13The exception on B6+ for Myene can be explained by the two grammar configurations we left out (see footnote7). Once reintegrated in Myene, B6+ has a higher precision than B6.

4.4. EXPERIMENTS AND DISCUSSION 85

and B6+ does not guarantee that the Adaptor Grammars will recover all the prefixes.

For one thing, sentences can be parsed ignoring these additional rules. But we also do not know whether all Mboshi prefixes are present or not in the data.

Hierarchy and complexity Recursivity is an important property of language, even considered a specificity of human language (Hauser et al.,2002;Everett,2005), and can be distributed differently across languages (Evans and Levinson, 2009). It is tightly linked to the presence of hierarchical structures in linguistic data. The notion of lin-guistic complexity,¹⁴ on the other hand, relates to recursivity but also involves other parameters, such as word or morpheme category and constituency. Additionally, lin-guistic complexity is sometimes understood in terms of the difficulty experienced by a language learner (Pallotti, 2015). To compare various levels of hierarchy and com-plexity, across or within languages, using AGs with a systematic grammar landscape and various word segmentation metrics, as we do here, opens the possibly to support quantitatively certain linguistic hypotheses relative to these notions. More specifically, A variants correspond to three different levels of hierarchy. At level B, variants B1, B2, B3 do not correspond to hypotheses of increasing hierarchical depth, but to hypothe-ses with different levels of complexity (e.g. distinguishing between morph’s positions);

conversely, B4, B5, and B6 introduce a new hierarchical level (Syllable), as well as an increasing morphological complexity. Lastly, C2 and C3 display the same hierarchical levels with comparable complexity, while C1 is simpler and flatter.

To understand how using AGs with this approach can help support quantitatively a linguistic hypothesis, we can look at word segmentation as a weak form of supervision for parsing. When a word segmentation is incorrect, the corresponding parse is also deemed to be incorrect. Conversely, if the word segmentation is correct, the parse might (or might not) be correct above and below. That is, the word segmentation task provides a way to rule out a fraction of the erroneous parses. The same reasoning can hold at other levels of description: identifying correct prefixes indicates a local adequacy of the parses and possibly above and below, while failing to accurately segment prefixes reveals an incorrect parse, and thus a flaw in the grammatical hypothesis. For that reason, we think that the methodology presented in this chapter can help to contrast grammatical descriptions: if it cannot formally validate a particular hypothesis, it can identify hypotheses yielding a larger quantity of possibly accurate parses.

It is interesting to visualize, in Figure 4.8, the regularities appearing for the vari-ous combinations of level A and B. The best performing grammars in terms of token F-measure¹⁵ correspond to the true average token length – this could not be otherwise.

But we can also clearly see how the combinations between levels A and B determine the average length of a word, and how augmenting the hierarchy at level A, all things otherwise equal, decreases this length, while augmenting the hierarchy at level B in-creases it. Using word segmentation as a weak supervision for parsing, i.e. ruling out grammars yielding on average a higher number of inaccurate parses, this suggest an iterative approach in which grammar descriptions could be successively refined (e.g.

“around” A3B3 for Mboshi, and A3B6+ for Myene in our experiments).

14A harder notion to define (Pallotti,2015).

15The same curves are observed for type F-measures.

Figure 4.8: Token F-measure plotted against average token length for all the grammars.

Colors and labels indicate hypotheses in levels A and B (only some labels are represented for readability). Vertical red lines indicate the true token average length for both corpora.

4.5. CONCLUSION 87

4.5 Conclusion

In this chapter, we were pursuing two main goals: i) improve upon strong baselines for the unsupervised discovery of words in two very low-resource Bantu languages; ii) explore the Adaptor Grammar framework as an analysis and prediction tool for linguists studying a new language.

Systematic experiments with 162 grammar configurations for each language have shown that using AGs for word segmentation is a way to test linguistic hypotheses during a language documentation process. Conversely, we have also shown that specializing a generic grammar with language specific knowledge greatly improves word segmentation performance.

In addition, the best word segmentation results using this approach are way higher than our Bayesian baselines, and the conjugated positive effects of non-terminal adap-tation and structure supervision by an expert are manifest. This proves the necessity to further foster collaborations between linguists and computer scientists, in order to speed up the documentation process and to meet the challenges of language documen-tation and preservation. The development of intuitive user interfaces, on one hand, and a particular attention to online learning approaches, on the other hand, could help make these collaborations more productive.

The main limit of the results presented in this chapter lie in the absence of bilingual grounding for the units we discover. A linguist could use discovered words or affixes to, then, identify regularities with respect to the translations. However, this process should be automated, and discovered units aligned to known units in the well-resourced language. This constitutes an important motivation for the work presented in Chapter6.

Chapter 5

Towards Tonal Models

Contents

5.1 Introduction . . . . 90 5.2 A preliminary study: supervised word segmentation . . . . 90 5.2.1 Data and representations . . . . 91 5.2.2 Disambiguating word boundaries with decision trees . . . . 91 5.3 Nonparametric segmentation models with tone information . . . . . 93 5.3.1 Language model . . . . 93 5.3.2 A spelling model with tones . . . . 94 5.4 Experiments and discussion. . . . 95 5.4.1 Representations . . . . 96 5.4.2 Tonal modeling . . . . 97 5.5 Conclusion . . . . 98

In Chapter 3, we established strong baselines for the unsupervised word segmenta-tion task in a low-resource scenario, and in Chapter 4 we greatly improved our results using expert knowledge. In this chapter, we investigate a question raised during pre-liminary experiments from Chapter 3: are tonal features useful for unsupervised word discovery? Considering Mboshi, we first show that tone annotation improves the per-formance of supervised learning when using a simplified representation of the data. To leverage this information in an unsupervised setting, we then propose two probabilistic models based on a hierarchical Pitman-Yor process that incorporates tonal representa-tions in its backoff structure. We compare these models with the tone-agnostic baseline already used in Chapter 3, and analyze if tone helps unsupervised segmentation on our small dataset. The work presented in this chapter is the result of a collaboration with Kevin Löser and has appeared, in part, in (Godard et al.,2018c). The code base used for the new models experimented here has been written by Kevin Löser (Löser and Al-lauzen,2016). We collaborated on the design and implementation of the new methods, and I contributed the preliminary study and the experiments.

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