In multi-turn interactions with deployed LLM assistants, users rarely provide explicit feedback, making it difficult to assess whether the interaction succeeded or failed. We therefore investigate user repair: follow-up user actions such as rephrasing, correcting, clarifying, or requesting more information. Such actions may signal breakdown recovery, but they can also reflect ordinary task progression, making them difficult to interpret in isolation. Using a human-annotated dataset of industrial multi-turn conversations, we examine when repair is informative about assistant-side deficiencies and user dissatisfaction. We find that repair occurs more often after deficient responses, but it is also common after non-deficient responses, making it a weak standalone signal of failure. Its diagnostic value becomes clearer when it is interpreted jointly with assistant response quality, repair type, and dialogue context. Repetition/rephrasing is most strongly associated with negative feedback on deficient responses, whereas requests for more information are more common after non-deficient responses, suggesting a role in collaborative task progression. We further show that repair is not always local: while most repairs target the immediately preceding turn, a substantial minority target earlier dialogue context. These findings position user repair as a context-dependent interactional signal for monitoring, evaluating, and diagnosing deployed assistants.

This paper investigates the use of bilingual word embeddings for mining Hiligaynon translations of English words. There is very little research on Hiligaynon, an extremely low-resource language of Malayo-Polynesian origin with over 9 million speakers in the Philippines (we found just one paper). We use a publicly available Hiligaynon corpus with only 300K words, and match it with a comparable corpus in English. As there are no bilingual resources available, we manually develop a English-Hiligaynon lexicon and use this to train bilingual word embeddings. But we fail to mine accurate translations due to the small amount of data. To find out if the same holds true for a related language pair, we simulate the same low-resource setup on English to German and arrive at similar results. We then vary the size of the comparable English and German corpora to determine the minimum corpus size necessary to achieve competitive results. Further, we investigate the role of the seed lexicon. We show that with the same corpus size but with a smaller seed lexicon, performance can surpass results of previous studies. We release the lexicon of 1,200 English-Hiligaynon word pairs we created to encourage further investigation.