Venue: NAACL 2025

# A Survey of NLP Progress in Sino-Tibetan Low-Resource Languages

Shuheng Liu and Michael Best

Georgia Institute of Technology

sliu775@gatech.edu, mikeb@gatech.edu

###### Abstract

Despite the increasing effort in including more low-resource languages in NLP/CL development, most of the world's languages are still absent. In this paper, we take the example of the Sino-Tibetan language family which consists of hundreds of low-resource languages, and we look at the representation of these low-resource languages in papers archived on ACL Anthology. Our findings indicate that while more techniques and discussions on more languages are present in more publication venues over the years, the overall focus on this language family has been minimal. The lack of attention might be owing to the small number of native speakers and governmental support of these languages. The current development of large language models, albeit successful in a few quintessential rich-resource languages, are still trailing when tackling these low-resource languages. Our paper calls for the attention in NLP/CL research on the inclusion of low-resource languages, especially as increasing resources are poured into the development of data-driven language models.

## 1 Introduction

Research on low-resource languages (LRLs) has become a major topic in the Natural Language Processing (NLP) and Computational Linguistics (CL) as seen by promotions from various conferences and workshops Magueresse et al. (2020), especially as we strive to develop multilingual large language models (LLMs) to incorporate more languages Mani and Namomsa (2023). ChatGPT OpenAI et al. (2024), one of the most successful commercially available LLMs, is claimed to support more than 80 languages Funelas (2024). However, this number is still far lower than the total number of languages in the world, which is estimated to be more than 7,000 Eberhard et al. (2024). As Joshi et al. (2020) pointed out, we would want to improve the linguistic diversity and inclusion in NLP/CL development, as "unsupervised pre-train methods only make the 'poor poorer'."

One common way to improve the performance of language models on LRLs is cross-lingual transfer learning Conneau et al. (2018), with which tasks in a target language are evaluated by models trained on source language(s). This technique has been found to work better when the source languages include languages from the same family as the target language Pandya and Bhatt (2024); Woller et al. (2021). A language family is a group of "genetically" related languages which all descended from the same ancestral language Nadasdy (1993). In this paper, we focus on the Sino-Tibetan (ST) language family, with the fourth largest number of languages Eberhard et al. (2024) and the second largest number of speakers Handel (2008). It consists of Sinitic languages such as Mandarin and Cantonese, and Tibeto-Burman languages Tibetan and Burmese, that descended from a hypothetical common ancestor Proto-Sino-Tibetan. There are over 1 billion native speakers of ST languages, mostly occupying "East Asia, peninsular Southeast Asia, and parts of South Asia" Handel (2008). A figure of the geographic distribution of Sino-Tibetan languages is shown in Appendix A.

Chinese1 has been the most focused ST languages by the NLP family such that the late ChatGPT is able to perform well on natural language tasks Fang et al. (2023); Li et al. (2023) and ERNIE bot (Baidu Research), a Chinese LLM, was developed not long after the release of ChatGPT. However, it is yet unknown how other ST languages are represented in the NLP literature, especially since they are all considered as URLs.

Footnote 1: The term “Chinese” in the context of NLP usually refers to Mandarin in its written form.

Our study attempts to answer on the following research questions:

* What is the overall and individual coverage of low-resource ST languages in NLP/CL literature?
* What are the main topics of these NLP/CL publications, and how have they changed over the years?
* What is the publication trend for ST languages in various NLP/CL conferences?

We focus on the papers archived in ACL Anthology, which contains publications from well-known NLP/CL conferences. We limit our study to include the low-resource ST languages that are spoken by more than 100,000 people, which are more likely to appear in NLP/CL literature. Our findings show that only 0.35% of all publications cover these ST languages. Less than half of the languages selected are covered, and more than 80% of the papers focus on just 5 languages. While it seems that the objective topics (aspects and methods of NLP/CL) covered by these publications have increased over the years, most publications have been and are still centered around 4 topics. Lastly, while we are excited to see the increasing number of publications in various conferences over the years, 81.34% of the papers are published in venues that draw less attention from the research community.

We start our paper with a brief literature review in SS 2, followed by our methodology in SS 3. We present our results in SS 4, and discuss their relevance in SS 5. Our paper acknowledges the progress of NLP/CL in ST LRLs, and simultaneously highlights areas for further development. We hope that our study fills the gap in this line of literature, calling attention to overlooked LRLs, and encouraging NLP/CL development with increasing diversity.

## 2 Literature Review

### NLP for LRLs

Joshi et al. (2020) categorized a language into one of the 6 classes, from class 0 (The Left-Behinds) to class 5 (The Winners), based on the availability of labeled and unlabeled data of that language. All languages in class 0, 1, 2, 3, and occasionally some languages in 4, appear as LRLs in various literature. While newer language models, especially multilingual models Devlin et al. (2019); Conneau et al. (2020) and large language models Workshop et al. (2023); OpenAI et al. (2024); Touvron et al. (2023), attempt to include languages from class 4, 3 and even 2 sometimes, the number of languages in class 0 and 1 still take up of more than 90% of world's languages.

To improve the model performance on LRLs tasks, there are usually two approaches. A direct approach would be to increase the amount of resource for LRLs, i.e. create more data for these languages. When there are unlabeled data for a language, researchers would be to create labeled data, either manually Nivre et al. (2020); Mayhew et al. (2024) or heuristically Pan et al. (2017); Wang et al. (2021). However, creating labeled data, especially manually, is known to be cost-ineffective Kang et al. (2023). Hedderich et al. (2021) provided an overview on the potential algorithmic and engineering solutions for developing NLP technology in low-resource settings. These include methods to create artificial data such as data augmentation Evuru et al. (2024); Lucas et al. (2024); Sobrevilla Cabezudo et al. (2024), and methods to effectively utilize the available resources such as cross-lingual transfer learning Conneau et al. (2018); Ruder et al. (2019), showing remarkable improvement on low-resource languages. In recent years, there has been more reliance on the power of LLMs due to their capability in zero-shot cross-lingual transfer Artetxe and Schwenk (2019) when all they need is unlabeled data.

### Survey Papers on Language Groups

Despite having the classification of language family, surveys often group languages based on their geographical distribution. For example, Zhang et al. (2024) presents a survey on neural machine translation for LRLs in China, albeit focusing on non-ST languages such as Mongolian and Uyghur. Other country-wide language studies include NLP progress for Ghanaian languages Azunre et al. (2021); Issaka et al. (2024) and Indian languages Khanuja et al. (2023); Vijayvergia et al. (2023), countries that are linguistically diverse with many LRLs. Such research can sometimes escalate to a continental level, such as for African languages Adebara and Abdul-Mageed (2022) and Latin American languages Tonja et al. (2024). While Gopal and Haroon (2016) attempts to discuss the Dravidian languages in general, the scope was limited only to 4 languages with the most speakers, although this was possibly due to the availability of language technologies being limited to just these 4 languages.

Language families are determined based on regu 

larity hypothesis, that is, languages are likely to be derived from the same parent language if there are numerous similarities Rowe and Levine (2015). Such similarities can still be detectable Kumar et al. (2021), which can potentially be exploited for various language modeling techniques. It is therefore imperative to understand what the research status of language technology on various language families is and address gaps in current research. To the best of our knowledge, there has not been any survey on the development of NLP/CL on the Sino-Tibetan language family as a whole. We intend to offer a first glance at this problem, and hope to draw more attention to this particular language family, LRLs, and language family studies.

## 3 Method

### Data Collection and Querying

Our study focuses on low-resource ST languages with more than 100,000 speakers reported by the World Atlas of Language Structures (WALS) Online Dryyer and Haspelmath (2013) under the Creative Commons Attribution 4.0 International License (CC BY 4.0), yielding 49 languages in total. As researchers sometimes use different names of the same language, e.g. "Bodo" and "Boro" for the Bodo language, or "Hokkien", "Southern Min" and "Min Nan" for the Southern Min language, we constructed a list of alternative language names to find relevant papers. Each language therefore contains a list of \(n\) query terms \(q_{1},q_{2},\cdots,q_{n}\). Some language names such as Bai coincide with common Asian surnames that could appear as references in a paper, and we added the word "language" to prevent querying thousands of irrelevant papers. The list of all languages and the names used can be found in Appendix B.

We focused on the papers available in ACL Anthology, which archives papers published in well-known international NLP and CL conferences as well as NLP conferences that have a regional focus. The ACL OCL corpus Rohatgi et al. (2023) contains extracted full text of 73,285 papers in ACL Anthology from 1952 to 2022 under the Creatie Commons Attribution-Noncommercial 4.0 International License (CC BY-NC 4.0).

For each language, we used regex string match to match papers in which one of the query terms of the language appeared in the ACL OCL corpus. The regex query that we used is

\[\verb|bq_{1}\verb|b|bq_{2}\verb|b|\cdots|bq_{n}\verb|b\]

where each query term is surrounded by the regex metacharacter b and joined with the \(|\) character. We also printed out the 200 characters before and after each matched query term to provide context for the next step. We repeated this for all 49 languages, resulting in 1,124 matched papers.

### Data Cleaning and Annotation

The previous step ensured that each matched paper contained a query term. However, the mention did not imply that the paper was building NLP tools for or conducting linguistic analysis of that language. For example, Tiedemann and Nakov (2013) wrote "The first step in our pivoting experiments involves SMT between closely related languages..., e.g. ..., **Cantonese**-Mandarin" which mentioned Cantonese as a background reference rather than built a translation tool for the language. Some query terms might coincide with mentions that are not about the corresponding languages. For example, Okabe et al. (2022) wrote "Japhug, a language from the Sino-**Tibetan** family" and was matched for the Tibetan language.

Using the 400-character context from the previous step, the first author examined all the matched papers only to retain papers relevant to our study of NLP/CL progress in ST languages (ST papers). Whenever the context is not enough to decide if a paper is relevant, the author would look at the original PDF of the paper. Eventually, we only retained 274 relevant matches (24.38% of regex-matched papers). We randomly sampled 100 papers from the rest of the ACL OCL corpus and found that none of them were relevant to our study, suggesting that there are unlikely any false negatives.

Rohatgi et al. (2023) defined 21 _objective topics_ based on the submission topics in major CL conferences, with which the author labeled papers. These objective topics are used as labels for the first author to assign to each paper. Each paper received at least one label based on its content. Eventually 16 of the 21 topics appeared in our dataset, namely dialogue and interactive systems (**Dialogue**); generation (**NLG**); information extraction (**IE**); interpretability and analysis of models for NLP (**Interpret**); linguistic theories, cognitive modeling and psycholinguistics (**LingTheory**); machine learning for NLP (**ML**); machine translation and multilinguality (**MT**); phonology, morphology and word segmentation (**WS**); question answering (**QA**); resources and evaluation (**Resource**); semantics: lexical semantics (**LexSem**); seman 

tics: sentence-level semantics, textual inference (**SenSem**); sentiment analysis, stylistic analysis, and argument mining (**Sentiment**); speech and multimodality (**Speech**); summarization (**Summ**) and syntax: tagging, chunking and parsing (**Syntax**).

## 4 Results

### ST Languages in ACL Anthology

Out of the 73K papers in the ACL OCL corpus, we only found 274 matches, i.e. 274 instances where a paper is discussing NLP or CL progress of one of the target languages. As each paper can be about more than one language, there are 253 unique ST papers (0.35%) found in our study.

Table 1 shows the distribution of the number of papers found for each language. More than half (26 out of 49, 53.06%) of our target languages did not yield any matches and 18 (36.73%) yielded fewer than 10 matches. Only five languages (10.20%), namely Burmese (57 matches), Cantonese (96 matches), Hakka (16 matches), Southern Min (17 matches) and Modern Literary Tibetan (44 matches), exceeded 10 matches, and they take up 83.94% of all the matches. This list highly correlates with the language taxonomy defined in Joshi et al. (2020). These five languages, plus Fuzhou and Wu, are the only 7 class 1 languages ("with some unlabeled data" and "have almost no labeled data"), whereas all other languages are class 0 languages ("with exceptionally limited resources" and "ignored from the perspective of language technologies"). The presence of 17 class 0 languages in Table 1 is encouraging, but it corroborates with the claim that these languages, along with those that are absent from this table, are largely overlooked by the NLP/CL community.

Moreover, Schwartz (2022) looked at 9,602 ACL abstracts between 2013 and 2021 and found that only 432 (4.50%) mentioned at least one language from the ST language family. This statistics would most likely be even lower when we leave out Chinese. This observation, combined with our finding of fewer than 300 ST papers, shows that despite being the 4th largest language family by the number of languages, low-resource ST languages are disproportionately represented in ACL Anthology.

### NLP Methods for ST Languages

Figure 1 shows the distribution of papers in each topic in each year. **LingTheory**, **Resource**, **MT** and **Speech** have almost always been focused on through the years, and more papers are being published on these topics as time progresses. We can also see a trend of more papers being published over the years, and more topics are covered by these papers in more recent years. However, despite spanning over 16 topics, more than half of the topics were only minimally covered, shown by the sporadic light cells in the right half of Figure 1.

While it is comforting to see that the CL objective topics covered for ST languages have increased over the years, Figure 2 re-emphasizes how the papers are disproportionately distributed towards the top languages. Although there is almost always at least one **LingTheory** paper for each language, the non-zero cells become more sparse as we move further to the right half of the figure. The diversity of the objective topics is, once again, mostly contributed by the top five languages (Burmese, Cantonese, Hakka, Southern Min and Modern Literary Tibetan), of which papers with the objective topics **SenSem**, **IE**, **Sentiment**, **Summ**, **Dialogue**, **NLG** and **QA** can only be found.

What we found in these two subsections is encouraging and alarming at the same time. On the one hand, Figure 1 shows a good prospect of more papers on low-resource ST languages covering a wider range of topics being published in the future. On the other hand, most papers cover, and most topics are discussed only for a few relatively higher-resource languages.

### Paper Distribution in Conferences

ACL Anthology collects papers from both ACL events as well as non-ACL events that publish CL papers. We then analyzed distribution of our dataset in these events and the changes from 2000

\begin{table}
\begin{tabular}{l|p{284.5pt}} \hline \hline
**\# Papers** & **Languages** \\ \hline \(<10\) & Ao, Arakanese (Marma), Bodo, Dimasa, Garo, Jingpho, Karen (Sgaw), Kok Borok, Lahu, Limbu, Lotha, Meithei, Mikir, Mizo, Naxi, Newari (Kathmandu), Tamang (Eastern), Wu \\ \hline \(10-49\) & Hakka, Min (Southern), Tibetan (Modern Literary) \\ \hline \(50-100\) & Burmese, Cantonese \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of papers found for each language. The left column is the range of the number of papers found, and the right column is the lists of languages.

 

Figure 1: Heat map showing the distribution of papers in each objective topic in each year. Darker cells represents more papers in a topic and year. Objective topics are sorted by the total number of papers, with the highest on the left and lowest on the right.

Figure 2: Heat map showing the distribution of papers in each topic for each language. Darker cells represents more papers in a topic and year. Objective topics are ordered by the total number of papers, with the highest on the left and lowest on the right.

 

to 2021. We excluded workshop papers, and omitted conferences with not more than five papers in our dataset. 134 ST papers across 9 conferences, namely ACL, IJCNLP, LREC, EMNLP, CCL, PACLIC, COLING, RCOLING and IJCL CLP, satisfy the criteria above (full names of these conferences are provided in Appendix C). As IJCNLP, LREC and COLING are held every two years, we put the papers into two-year buckets to capture the general trend. We also calculated the percentages of all ST papers published in each two-year bucket. The results are shown in Figure 3.

We observe from Figure 3 that as the number of publications increases over the years, these publications are also distributed in a more diverse range of conferences, from only 3 in 2000-2001 to 8 in 2020-2021. PACLIC, LREC and ACL comprise of 55.22% of all papers. The number of paper published in EMNLP, being the third most impactful conference hosted on ACL Anthology Eickhoff (2023), is observed to be growing, as well as CCL, the largest NLP conference in China focusing on languages in China,2 whose papers are only featured in ACL Anthology after 2020.

Footnote 2: [http://cips-cl.org/static/CCL2024/en/index.html](http://cips-cl.org/static/CCL2024/en/index.html)

The percentage of ST papers mostly fluctuated between 0.20% to 0.30%, but the percentage has increased to 0.36% in the most recent 2020-2021 bucket. Despite the low percentage, the general upward trend gives us some confidence that the inclusion of ST languages is growing.

Nevertheless, ACL and EMNLP are the only ACL events in this list, while 81.34% of the papers are published in non-ACL events. The h5-index3 of ACL is 192 and 176 for EMNLP. COLING (73), LREC (61) and IJCNLP (24) have a significantly lower h5-index, and those for the other four conferences were not found.4 We are not suggesting that the h5-index informs the quality of the publication venues, but it can deduce that these publications have not received enough attention.

Footnote 3: h5-index is the h-index for articles published in the last 5 complete years.

Footnote 4: Data from Google Scholar and Research.com

## 5 Discussion

### NLP Research in ST Languages

In contrast to the increasing recognition and focus in the study of multilingual NLP and LRLs, exemplified by the inclusion of the "Linguistic Diversity" and "Multilingualism and Cross-lingual NLP" tracks in the recent ACL 2023 Rogers et al. (2023), our results in Section 4.1 show that ST languages have been disproportionately represented in the NLP literature. Even when we look at the languages covered in multilingual corpora containing hundreds of thousands of languages, only a few ST languages are usually included. Table 2 presents a few highly cited multilingual corpora and their coverage on ST languages. From Table 2, we can see that in most corpora, only \(\leq 5\%\) languages are in the ST family, including Simplified/Traditional Chinese which is always present. Similar to Table 1, Burmese, Cantonese (Yue Chinese) and Tibetan are the next most popular choices. Even when some other LRLs are included, they usually make up of a small percentage of the entire dataset, such as in WikiAnn Pan et al. (2017) named entity recognition (NER) dataset, where all ST languages other than Chinese (Mandarin) amount to 131K name mentions, slightly more than the name mentions from Vietnamese alone (125K), and without Southern Min Chinese (Min Nan)) and Cantonese, this number drops to only 31K.

So what makes Burmese, Cantonese, Tibetan, Southern Min and Hakka from Table 1 different from the other ST LRLs? We believe that there are two explanations to this question, in addition to the fact that they are class 1 languages.

The first explanation is the number of native speakers of these languages. Cantonese, Southern Min, Hakka and Burmese have the 1st, 3rd, 4th and 5th most speakers of all low-resource ST languages Dryer and Haspelmath (2013). While Central Tibetan has the 15th most speakers, this number might have only included the U-Tsang (Central) Tibetan speakers. As the three main dialects of Tibetan (or the three main Tibetic languages, i.e. U-Tsang, Khams and Amdo Tibetan) are not used as written languages, and Classical Literary Tibetan has been and still is widely used for writing Tournadre (2014), the number of speakers (or users) might be much higher when the language name is only specified as "Tibetan" in these corpora. Simply adding the speaker population of these three dialects raises the ranking of Tibetan to the 7th.

The absence of the 2nd (Wu) and 6th (Fuzhou) languages with the most speakers, which are also the class 1 languages, brings out the second explanation to this question - the lack of support and recognition of these languages as individual "languages". Burmese is the official language of Myanmar and even though Cantonese Bolton (2011), 

Southern Min and Hakka [35], and Tibetan [26] are not official languages of any state, they have received various level of governmental support and attention. In contrast, Wu and Fuzhou are widely recognized as simply "Chinese dialects", which is justified politically and culturally, but not linguistically [10]. Such lack of recognition as individual languages could result in NLP/CL researchers without training in Linguistics to ignore the development of technology for these languages. Even with researchers working on these languages, speakers would usually engage in verbal communication in these languages, instead of written [48], causing a lack of textual data available for the NLP community. Additionally, despite some, if not all, non-Sinitic languages being recognized by local authorities, many (in China as an example) are facing the lack of specific legislation for minority language planning [27]. Many areas in which Tibeto-Burman languages are spoken are also challenging for researchers to access and collect data [9]. These might hinder the development of the languages themselves, as well as the NLP/CL research on these languages.

### LRLs in the LLM Era

Since the release of ChatGPT in 20225, there have been more and more large language models (LLMs) developed [24, 34]. The performances of these LLMs rely heavily on

\begin{table}
\begin{tabular}{l|p{142.3pt}|p{142.3pt}} \hline \hline
**Corpus** & **\# Langs** & **ST Languages** \\ \hline FLORES-200 [34] & 204 & Standard Tibetan, Dzongkha, Jingpho, Mizo, Meitei, Burmese, Yue Chinese, Chinese \\ OSCAR [1] & 153 & Burmese, Chinese, Newari, Tibetan, Wu Chinese \\ ROOTS [10] & 46 & Simplified Chinese, Traditional Chinese \\ UD [24] & 90 & Cantonese, Chinese, Classical Chinese \\ WikiAnn [26] & 282 & Tibetan, Min Dong Chinese, Dzongkha, Gan Chinese, Hakka Chinese, Sichuan Yi, Burmese, Newari, Wu Chinese, Classical Chinese, Chinese (Min Nan), Cantonese, Chinese \\ XTREME [25] & 40 & Burmese, Mandarin \\ \hline \hline  & \multicolumn{1}{c}{} & \multicolumn{1}{p{142.3pt}}{} \\ \end{tabular}
\end{table}
Table 2: Examples of large multilingual corpora with the total number of languages and the ST languages they cover. The names of the languages appear as how they are defined in the corresponding corpus.

Figure 3: Number of papers about ST languages (ST papers) in different conferences between 2000 and 2021. The papers are grouped into two-year buckets as IJCNLP, LREC and COLING are held biannually. The width of the band of each color represents the number of papers in the corresponding conference in each two-year bucket. The dashed line plot shows the trend of the percentage of ST papers published in two-year buckets.

 

the amount of textual data available (Hoffmann et al., 2024) to be proficient in multiple languages. Despite the high performance of LLMs on major languages in the world, that on LRLs is still trailing (Avetisyan and Broneske, 2023; Robinson et al., 2023; Adelani et al., 2024).

We therefore conducted an additional experiment by running ChatGPT (_gpt-3.5-turbo_) on the low-resource ST languages from the WikiAnn NER dataset (Pan et al., 2017).6 The method of the experiment can be found in Appendix D, and we record the micro F\({}_{1}\) scores in Table 3.

Footnote 6: There are no data for Sichuan Yi despite the language appearing in the paper.

From Table 3, we observe that the F\({}_{1}\) scores for the Tibeto-Burman languages, i.e. Burmese (4.13%), Dzongkha (12.12%), Newari (9.39%), Tibetan (3.60%) are extremely low. The F\({}_{1}\) scores of 4 of the 6 Sinitic languages, Cantonese (7.06%), Gan Chinese (17.82%), Hakka (29.18%), Min Dong Chinese (20.41%), are lower than the performance on Weibo (30.09%), the lowest from Xie et al. (2023). This resonates with the results from previous findings that LLMs are trailing behind on LRLs. Additionally, even the highest F\({}_{1}\) score in this table is merely 35.97%. While ChatGPT has been shown not to perform on par with the state-of-the-art fine-tuning models on the commonly used English NER dataset (Tjong Kim Sang and De Meulder, 2003) at only 53.5% (Qin et al., 2023), the numbers shown in Table 3 are still much lower. This highlights the gap between ChatGPT's performance on high- and low-resource languages.

The _curse of multilinguality_, i.e. degradation of multilingual models on individual languages due to model capacity (Conneau et al., 2020), is known to harm the performance of language models on LRLs Wu and Dredze (Wu and Dredze, 2020). While methods to mitigate such phenomenon have been proposed Pfeiffer et al. (2022); Blevins et al. (2024), we are yet to know if LLMs such as ChatGPT have incorporated these methods, or if these methods can be generalized to trillion-parameter models.

### Future Directions and Recommendations

One of the first challenges that we encountered was the difficulty to find natural language processing papers on specific natural languages. Ducel et al. (2022) found that publications in LREC tend to respect the #BenderRule ("Always name the language(s) you're working on") (Bender, 2021) more than those in ACL. Even when languages are cited in the publications, there can be more efficient ways developed to query these publications, such as adding keywords to make the searches easier, or have the authors indicate clearly the language(s) at which their papers are targeting as part of the metadata of the paper.

Our paper, while only focusing on the ST language family, hopes to call for more NLP/CL research on the effect of using languages from the same family. In addition to cross-lingual transfer Pandya and Bhatt (2024); Woller et al. (2021), language family has also shown to be helpful for data augmentation Scalvini and Debess (2024). Moreover, as languages in the same family often share cognates and regular sound changes, such features can potentially be utilized by language models to generalize to LRLs in the same family as higher-resource languages, with multimodal language models or language models trained with phonetic/phonological knowledge. The NLP/CL progress on language family might even reciprocally benefit the historical linguists in their determination of language families Kondrak (2009).

While most NLP research on LRLs works under the low-resource restriction, many tried to tackle the problem by creating more resource for these languages. More recently, effort has been put into groups of LRLs, such as African languages by

\begin{table}
\begin{tabular}{l|c} Language & F\({}_{1}\) (\%) \\ \hline Tibetan & 3.60 \\ Burmese & 4.13 \\ Cantonese* & 7.06 \\ Newari* & 9.39 \\ Dzongkha & 12.12 \\ Gan Chinese & 17.82 \\ Min Dong Chinese & 20.41 \\ Hakka & 29.18 \\ Chinese (Min Nan)* & 33.94 \\ Wu Chinese & 35.97 \\ \hline Weibo Peng and Dredze (2015) & 30.09 \\ Onto. 47  & 33.74 \\ MSRA Zhang et al. (2006) & 45.51 \\ \end{tabular}
\end{table}
Table 3: F\({}_{1}\) scores of ChatGPT on the low-resource ST languages in WikiAnn in ascending order. The three results from the bottom are the performance reported by Xie et al. (2023) on three general-domain Chinese datasets using the vanilla zero-shot method. *Only the first 3,000 sentences were used for evaluation.

 

Masakha NLP (Adelani et al., 2021; Dione et al., 2023; Adelani et al., 2023) and Southeast Asian languages (Lovenia et al., 2024). From Figure 2, despite **Resource** consisting the second most papers, not only does this topic also contains evaluation papers, but they are also highly concentrated on a few languages. An example of such lack of unlabeled data is the distribution of languages8 for CommonCrawl9, which one of the widely used corpora collected from the Internet for training language models. For ST languages, the Commonwealth corpus only contains the Chinese languages (not specified which ones), Tibetan, Burmese and Dzongkha. This lack of unlabeled textual data alludes to the lack of online discourses in these LRLs, and potentially the lack of online communities of speakers of these LRLs. A future direction for researchers can be creating more resources for these languages, even just unlabeled data. In addition to collecting data, researchers may think of ways to foster online communities for LRL speakers.

Footnote 7: [https://commoncrawl.github.io/cc-crawl-statistics/plots/language.html](https://commoncrawl.github.io/cc-crawl-statistics/plots/language.html), accessed on Nov. 23rd, 2024.

Footnote 8: [https://commoncrawl.github.io/cc-crawl-statistics/plots/lingauges.html](https://commoncrawl.github.io/cc-crawl-statistics/plots/lingauges.html), accessed on Nov. 23rd, 2024.

## 6 Conclusion

Our paper is the first in the field to provide a systematic look into the coverage of ST languages in NLP/CL literature. From our analyses, we acknowledge the encouraging improvement made by the NLP/CL community to include more ST languages over the years, and have a wide range of techniques and discussion topics about these languages. However, we also raise concerns on the low count of publications, covering only part of our list of languages, despite the list having already excluded hundreds of ST languages with even lower number of native speakers. The publication venues have also become more diverse, but most papers possibly did not receive enough attention from the research community. The existing disparity of the literature coverage and model performance on ST LRLs as compared to high-resource languages may be worsened by the advancement of LLMs, relying on large quantity of unlabeled data, making the "poor poorer" (Joshi et al., 2020). We hope that this paper calls for more research into ST languages and ways to improve the status of LRLs.

### Limitations

The queries we used in Section 3.1 to reduce false positives might still overshoot to exclude publications that should be in our dataset, even after manually checking a random sample. One reviewer pointed out that a lot of metadata of submissions, including the languages studied, are collected but not released for publications. These metadata could allow future analysis similar to this paper to provide insights on how the field of NLP/CL can advance. We hope that in the future, conferences can include the language(s) that are studied in each paper. Additionally, we would like to call for NLP/CL researchers to clarify the language(s) that they studied in their publications.

One reviewer pointed out that there could be some more fine-grained classification provided in Section 4.2 to generate more insights on the research bias. We agree with the reviewer and attempted a small-scale analysis, but still decide to leave more rigorous analyses for future work.

While ACL Anthology collects publications in major NLP events, NLP literature, especially regarding indigenous ST languages, can be published in other venues. Additionally, almost all of the papers in our dataset are published in English, while there can be publications in other languages. Future work may explore literature in other Computer Science/Computational Linguistics venues as well as in other languages, especially Chinese, Burmese and various official languages in India.

## Acknowledgments

We thank the three anonymous reviewers for their recognition and valuable feedback. Our gratitude also extends to Karthik S. Bhat, Terra Blevins, Amy Z. Chen, Zev Handel, Aman Khullar and Daniel Nkempleu for their support and discussions on various ideas and aspects of this paper. We would also like to thank the rest of the Georgia Tech T+ID lab for their feedback on earlier versions of this paper.

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## Appendix A Sino-Tibetan Language Distribution

Figure 4 shows the distribution and classifications of Sino-Tibetan languages.

## Appendix B Language Names Used

We list all the languages and the names that we used for query in Table 4 below: 

Conference Names

We include the full names of the conferences that appeared in our analysis:

1. **ACL**: Annual Meeting of the Association for Computational Linguistics
2. **IJCNLP**: International Joint Conference on Natural Language Processing
3. **LREC**: International Conference on Language Resources and Evaluation
4. **EMNLP**: Conference on Empirical Methods in Natural Language Processing
5. **CCL**: Chinese National Conference on Computational Linguistics
6. **PACLIC**: Pacific Asia Conference on Language, Information and Computation
7. **COLING**: International Conference on Computational Linguistics
8. **ROCLING**: Conference on Computational Linguistics and Speech Processing
9. **IJCLCLP**: International Journal of Computational Linguistics and Chinese Language Processing

## Appendix D Method for Mini Experiment

We modified the zero-shot vanilla method proposed in Xie et al. (2023) by adding the example given in the paper. The prompt is as follows:

Given entity label set: {location, organization, person, miscellaneous} Based on the given entity label set, please recognize the named entities in the giventext. Example: Text: Could Tony Blairbeinline linefor a gold medal? Answer: {'TonyBlair': 'person'} Text: <sentence> Answer: where <sentence> is a space-separated sentence constructed by concatenating words of the sentence in the dataset. We used the default setting of _gpt-3.5-turbo_ and passed the prompt using the ChatGPT API10 to generate an answer. We then parsed the answer given by ChatGPT, compared it to the gold labels and calculated the micro F\({}_{1}\) score.

 

Figure 4: The distribution of Sino-Tibetan languages. Attribution: By GalaxMaps - Own work, CC BY-SA 4.0, [https://commons.wikimedia.org/w/index.php?curid=115399902](https://commons.wikimedia.org/w/index.php?curid=115399902)





\begin{table}
\begin{tabular}{|l|p{284.5pt}|} \hline
**Language** & **Query Terms** \\ \hline Akha & Aka \\ \hline Amdo & Amodo, Ando Tibetan \\ \hline Angami & Angami \\ \hline Ao & Ao Naga, Ao language, Ao \\ \hline Arakanese (Marma) & Marma, Arakanese, Marma Arakanese, Rakhine \\ \hline Bai & Bai language \\ \hline Balti & Balti \\ \hline Bodo & Bodo, Boro \\ \hline Bokar & Adi, Bokar, Milang \\ \hline Burmese & Burmese \\ \hline Cantonese & Yue Chinese, Cantonese \\ \hline Chin (Tiddim) & Tedim, Tedim Chin, Tiddim, Tiddim Chin \\ \hline Dimasa & Dimasa \\ \hline Fuzhou & Furzhou, Foochow, Hokchew, Hok-chiu, Fuzhounese, Min Dong Chinese \\ \hline Garo & Garo \\ \hline Gurung & Gurung, Tamu Kyi, Tamu Bhasa \\ \hline Hakka & Hakka, Hakka Chinese \\ \hline Hani & Hani \\ \hline Hyow & Hyow, Asho, Asho Chin \\ \hline Jingpho & Jingpho, Jingpong, Jinghpaw, Kachin \\ \hline Karen (Pwo) & Pwo, Pwo Karen \\ \hline Karen (Sgaw) & S’gaw, S’gaw Karen, S’gaw K'Nyaw \\ \hline Kham (Tibetan) (Nangchen) & Khams Tibetan, Khams \\ \hline Kokborok & Kokborok, Kok Borok \\ \hline Lahu & Lahu \\ \hline Lai & Hakha, Hakha Chin, Laiholh \\ \hline Limbu & Limb \\ \hline Lisu & Lisu, List language \\ \hline Lotha & Lotha, Lotha Naga \\ \hline Magar & Eastern Magar \\ \hline Magar (Syangja) & Syangja, Magar Syangja, Syangja Magar, Western Magar \\ \hline Maru & Liao Vo, Maru, Lhaovo \\ \hline Meithei & Meithei, Meitei \\ \hline Mikir & Mikir, Karbi \\ \hline Min (Southern) & Min Nan, Min Nan Chinese, Hokkien, Southern Min, Banlam \\ \hline Mising & Missing \\ \hline Mizo & Mizo \\ \hline Naga (Zeme) & Zeme \\ \hline Naxi & Naxi, Nakhi, Nasi, Lomi, Moso, Mo-su \\ \hline Newari (Kathmanduh) & Newar, Newari \\ \hline Nuosu & Nuosu, Nosu, Northern Yi, Liangshan Yi, Sichuan Yi \\ \hline Nyishi & Nyishi, Nishi, Nishang, Nissi, Nbyising, Leil, Aya, Akang, Bangni-Bangru, Solving \\ \hline Sherpa & Sherpa \\ \hline Tamang (Eastern) & Tamang \\ \hline Thadou & Thadou, Thado Chin \\ \hline Tibetan (Modern Literary) & Tibetan \\ \hline Tshangla & Tshanglela \\ \hline Wu & Wu Chinese, Suzhonuese, Shanghaiinese \\ \hline Yi (Wuding-Luquan) & Luquan Yi, Wudding Yi, Nasu \\ \hline \end{tabular}
\end{table}
Table 4: The complete list of all languages covered in our study and the query terms that we used when searching through the corpus